Repository: CURENT/andes (language: python)

Issue:
Demo of DGPRCT
Added example input file and demo notebook for `DGPRCT`.


Discussion / hints:

Fix patch:
diff --git a/.flake8 b/.flake8
index b1fe3ebe1..5b1e3952e 100644
--- a/.flake8
+++ b/.flake8
@@ -1,3 +1,3 @@
 [flake8]
-exclude = .git,__pycache__,build,dist,demos,cases,cards,versioneer.py,texttable.py,andes/_version.py,docs/source/conf.py,benchmarks,andes/pycode,scripts
+exclude = .git,__pycache__,build,dist,demos,cases,cards,versioneer.py,texttable.py,andes/_version.py,docs/source/conf.py,benchmarks,andes/pycode,scripts,.history
 max-line-length=115
diff --git a/.gitignore b/.gitignore
index 2e2a953e9..0258c7228 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 # Byte-compiled / optimized / DLL files
 __pycache__/
+.history/
 *.py[cod]
 *$py.class
 
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 000000000..b7e1e00ec
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,5 @@
+repos:
+-   repo: https://gitlab.com/pycqa/flake8
+    rev: '3.9.2'
+    hooks:
+    -   id: flake8
diff --git a/andes/cases/ieee14/ieee14_dgprct1.xlsx b/andes/cases/ieee14/ieee14_dgprct1.xlsx
new file mode 100644
index 000000000..000c229e9
Binary files /dev/null and b/andes/cases/ieee14/ieee14_dgprct1.xlsx differ
diff --git a/andes/cases/ieee14/ieee14_dgprctext.xlsx b/andes/cases/ieee14/ieee14_dgprctext.xlsx
new file mode 100644
index 000000000..17d450cc1
Binary files /dev/null and b/andes/cases/ieee14/ieee14_dgprctext.xlsx differ
diff --git a/andes/cases/kundur/kundur_vsc.xlsx b/andes/cases/kundur/kundur_vsc.xlsx
index bd4a351a9..5c58aaf38 100644
Binary files a/andes/cases/kundur/kundur_vsc.xlsx and b/andes/cases/kundur/kundur_vsc.xlsx differ
diff --git a/andes/core/block.py b/andes/core/block.py
index f173f8aed..71bfb7e06 100644
--- a/andes/core/block.py
+++ b/andes/core/block.py
@@ -632,7 +632,7 @@ class IntegratorAntiWindup(Block):
     The initial output must be specified through `y0`.
     """
 
-    def __init__(self, u, T, K, y0, lower, upper, name=None, tex_name=None, info=None):
+    def __init__(self, u, T, K, y0, lower, upper, name=None, tex_name=None, info=None, no_warn=False):
         super().__init__(name=name, tex_name=tex_name, info=info)
         self.u = dummify(u)
         self.T = dummify(T)
@@ -641,11 +641,12 @@ def __init__(self, u, T, K, y0, lower, upper, name=None, tex_name=None, info=Non
         self.lower = dummify(lower)
         self.upper = dummify(upper)
         self.enforce_tex_name((self.K, self.T))
+        self.no_warn = no_warn
 
         self.y = State(info='AW Integrator output', tex_name='y', t_const=self.T)
 
         self.lim = AntiWindup(u=self.y, lower=self.lower, upper=self.upper, tex_name='lim',
-                              info='Limiter in integrator',
+                              info='Limiter in integrator', no_warn=self.no_warn,
                               )
 
         self.vars = {'y': self.y, 'lim': self.lim}
diff --git a/andes/core/discrete.py b/andes/core/discrete.py
index 1e0cf5cce..6966ee0c8 100644
--- a/andes/core/discrete.py
+++ b/andes/core/discrete.py
@@ -510,10 +510,10 @@ class AntiWindup(Limiter):
         by the anti-windup-limiter.
     """
 
-    def __init__(self, u, lower, upper, enable=True,
+    def __init__(self, u, lower, upper, enable=True, no_warn=False,
                  no_lower=False, no_upper=False, sign_lower=1, sign_upper=1,
                  name=None, tex_name=None, info=None, state=None):
-        super().__init__(u, lower, upper, enable=enable,
+        super().__init__(u, lower, upper, enable=enable, no_warn=no_warn,
                          no_lower=no_lower, no_upper=no_upper,
                          sign_lower=sign_lower, sign_upper=sign_upper,
                          name=name, tex_name=tex_name, info=info)
@@ -521,6 +521,7 @@ def __init__(self, u, lower, upper, enable=True,
 
         self.has_check_var = False
         self.has_check_eq = True
+        self.no_warn = no_warn
 
     def check_var(self, *args, **kwargs):
         """
diff --git a/andes/core/model.py b/andes/core/model.py
index 5a1c9c3d4..9fc01f0d0 100644
--- a/andes/core/model.py
+++ b/andes/core/model.py
@@ -1055,13 +1055,12 @@ def s_update_var(self):
         if len(self.services_var):
             kwargs = self.get_inputs()
             for name, instance in self.services_var.items():
-                if instance.v_str is None:
-                    continue
-                func = self.calls.s[name]
-                if callable(func):
-                    instance.v[:] = func(*self.s_args[name])
+                if instance.v_str is not None:
+                    func = self.calls.s[name]
+                    if callable(func):
+                        instance.v[:] = func(*self.s_args[name])
 
-            # Apply individual `v_numeric`
+                # Apply individual `v_numeric`
                 func = instance.v_numeric
                 if func is not None and callable(func):
                     instance.v[:] = func(**kwargs)
diff --git a/andes/core/var.py b/andes/core/var.py
index 627bc3312..b166071da 100644
--- a/andes/core/var.py
+++ b/andes/core/var.py
@@ -87,19 +87,25 @@ def __init__(self,
         self.diag_eps = diag_eps        # small diagonal value to be added to `dae.gy`
         self.deps = deps          # a list of variable names this BaseVar depends on for initialization
 
-        # attributes assigned by `set_address`
+        # --- attributes assigned by `set_address` begins ---
         self.n = 0
-        self.a: np.ndarray = np.array([], dtype=int)       # address array
-        self.r: np.ndarray = np.array([], dtype=int)       # equation RHS value array for external equations
-        self.v: np.ndarray = np.array([], dtype=float)     # variable value array
-        self.e: np.ndarray = np.array([], dtype=float)     # equation value array
+
+        # address into the variable and equation arrays (dae.f/dae.g and dae.x/dae.y)
+        self.a: np.ndarray = np.array([], dtype=int)
+
+        # address into external equation RHS array (dae.h/dae.i)
+        self.r: np.ndarray = np.array([], dtype=int)
 
         self.av: np.ndarray = np.array([], dtype=int)      # FIXME: future var. address array
         self.ae: np.ndarray = np.array([], dtype=int)      # FIXME: future equation address array
+        # --- attributes assigned by `set_address` ends ---
+
+        self.v: np.ndarray = np.array([], dtype=float)  # variable value array
+        self.e: np.ndarray = np.array([], dtype=float)  # equation value array
 
         # internal flags
         # NOTE:
-        # contiguous is only True for internal variables of models with flag ``collate`` equal to ``False``.
+        # contiguous is True only for internal variables of models with flag `collate = False`.
         self._contiguous = False  # True if if address is contiguous to allow slicing into arrays without copy.
 
         self.e_inplace = False    # True if `self.e` is in-place access to `System.dae.__dict__[self.e_code]`
@@ -446,6 +452,16 @@ def link_external(self, ext_model):
                                    allow_none=self.allow_none,
                                    default=0,
                                    ).astype(int)
+
+            # check if source var type is the same as this ExtVar
+            vcodes = np.array(ext_model.get_field(src=self.src, idx=self._idx, field='v_code'))
+            vcodes = vcodes[vcodes != np.array(None)].astype(str)
+
+            if not all(vcodes == np.array(self.v_code)):
+                raise TypeError("ExtVar <%s.%s> is of type <%s>, but source Vars <%s.%s> may not." %
+                                (self.owner.class_name, self.name, self.v_code,
+                                 ext_model.class_name, self.src))
+
             self.n = len(self.a)
 
         else:
diff --git a/andes/models/__init__.py b/andes/models/__init__.py
index cbb971f9d..250d3be53 100644
--- a/andes/models/__init__.py
+++ b/andes/models/__init__.py
@@ -30,10 +30,10 @@
     ('measurement', ['BusFreq', 'BusROCOF', 'PMU']),
     ('dcbase', ['Node', 'Ground', 'R', 'L', 'C', 'RCp', 'RCs', 'RLs', 'RLCs', 'RLCp']),
     ('vsc', ['VSCShunt']),
-    ('renewable', ['REGCA1', 'REECA1', 'REECA1E',
+    ('renewable', ['REGCA1', 'REECA1', 'REECA1E', 'REECA1G',
                    'REPCA1', 'WTDTA1', 'WTDS', 'WTARA1', 'WTPTA1', 'WTTQA1', 'WTARV1',
                    'REGCVSG', 'REGCVSG2']),
-    ('distributed', ['PVD1', 'ESD1', 'EV1', 'EV2', 'PLK']),
+    ('distributed', ['PVD1', 'ESD1', 'EV1', 'EV2', 'DGPRCT1', 'DGPRCTExt']),
     ('experimental', ['PI2', 'TestDB1', 'TestPI', 'TestLagAWFreeze', 'FixedGen']),
     ('coi', ['COI']),
 ])
diff --git a/andes/models/distributed/__init__.py b/andes/models/distributed/__init__.py
index 8328ceb17..1536e8736 100644
--- a/andes/models/distributed/__init__.py
+++ b/andes/models/distributed/__init__.py
@@ -1,4 +1,4 @@
 from andes.models.distributed.pvd1 import PVD1  # NOQA
 from andes.models.distributed.esd1 import ESD1  # NOQA
-from andes.models.distributed.plk import PLK  # NOQA
 from andes.models.distributed.ev import EV1, EV2  # NOQA
+from andes.models.distributed.dgprct import DGPRCT1, DGPRCTExt  # NOQA
diff --git a/andes/models/distributed/dgprct.py b/andes/models/distributed/dgprct.py
new file mode 100644
index 000000000..cf527edc0
--- /dev/null
+++ b/andes/models/distributed/dgprct.py
@@ -0,0 +1,598 @@
+"""
+Distributed energy resource protection model base.
+"""
+from andes.core.param import IdxParam, NumParam, ExtParam
+from andes.core.model import Model, ModelData
+from andes.core.var import Algeb, ExtAlgeb
+from andes.core.service import ConstService, ExtService, ExtendedEvent
+from andes.core.discrete import Limiter
+from andes.core.block import IntegratorAntiWindup
+
+
+class DGPRCTBaseData(ModelData):
+    """
+    DGPRCT Base model data.
+    """
+
+    def __init__(self):
+        super(DGPRCTBaseData, self).__init__()
+
+        self.dev = IdxParam(info='idx of the target device',
+                            mandatory=True,
+                            )
+
+        self.busfreq = IdxParam(model='BusFreq',
+                                info='Target device interface bus measurement device idx',
+                                )
+
+        # -- protection enable parameters
+        self.fen = NumParam(default=1,
+                            tex_name='fen',
+                            vrange=(0, 1),
+                            info='Frequency deviation protection enable. \
+                                  1 for enable, 0 for disable.',
+                            )
+
+        # TODO: add `fflag` for choice that whether block source signal
+        # self.fflag = NumParam(default=1,
+        #                       tex_name='fflag',
+        #                       vrange=[0, 1],
+        #                       info='Frequency lock option. \
+        #                             1 to enable freq after protection, \
+        #                             0 to disable freq after protection.',
+        #                       )
+
+        self.Ven = NumParam(default=0,
+                            tex_name='Ven',
+                            vrange=(0, 1),
+                            info='Voltage deviation protection enable.\
+                                  1 for enable, 0 for disable.',
+                            )
+
+        # -- protection parameters, frequency
+        self.fl3 = NumParam(default=50,
+                            tex_name='fl3',
+                            info='Under frequency shadding point 3',
+                            unit='Hz',
+                            )
+
+        self.fl2 = NumParam(default=57.5,
+                            tex_name='fl2',
+                            info='Over frequency shadding point 2',
+                            unit='Hz',
+                            )
+
+        self.fl1 = NumParam(default=59.2,
+                            tex_name='fl1',
+                            info='Under frequency shadding point 1',
+                            unit='Hz',
+                            )
+
+        self.fu1 = NumParam(default=60.5,
+                            tex_name='fu1',
+                            info='Over frequency shadding point 1',
+                            unit='Hz',
+                            )
+
+        self.fu2 = NumParam(default=61.5,
+                            tex_name='fu2',
+                            info='Over frequency shadding point 2',
+                            unit='Hz',
+                            )
+
+        self.fu3 = NumParam(default=70,
+                            tex_name='fu3',
+                            info='Over frequency shadding point 3',
+                            unit='Hz',
+                            )
+
+        self.Tfl1 = NumParam(default=300,
+                             tex_name=r'T_{fl1}',
+                             info='Stand time for (fl2, fl1)',
+                             non_negative=True,
+                             )
+
+        self.Tfl2 = NumParam(default=10,
+                             tex_name=r'T_{fl2}',
+                             info='Stand time for (fl3, fl2)',
+                             non_negative=True,
+                             )
+
+        self.Tfu1 = NumParam(default=300,
+                             tex_name=r'T_{fu1}',
+                             info='Stand time for (fu1, fu2)',
+                             non_negative=True,
+                             )
+
+        self.Tfu2 = NumParam(default=10,
+                             tex_name=r'T_{fu2}',
+                             info='Stand time for (fu2, fu3)',
+                             non_negative=True,
+                             )
+
+        # -- protection parameters, voltage
+        self.vl4 = NumParam(default=0.1,
+                            tex_name='vl4',
+                            info='Under voltage shadding point 4',
+                            unit='p.u.',
+                            )
+
+        self.vl3 = NumParam(default=0.45,
+                            tex_name='vl3',
+                            info='Under voltage shadding point 3',
+                            unit='p.u.',
+                            )
+
+        self.vl2 = NumParam(default=0.6,
+                            tex_name='vl2',
+                            info='Under voltage shadding point 2',
+                            unit='p.u.',
+                            )
+
+        self.vl1 = NumParam(default=0.88,
+                            tex_name='vl1',
+                            info='Under voltage shadding point 1',
+                            unit='p.u.',
+                            )
+
+        self.vu1 = NumParam(default=1.1,
+                            tex_name='vu1',
+                            info='Over voltage shadding point 1',
+                            unit='p.u.',
+                            )
+
+        self.vu2 = NumParam(default=1.2,
+                            tex_name='vu2',
+                            info='Over voltage shadding point 2',
+                            unit='p.u.',
+                            )
+
+        self.vu3 = NumParam(default=2,
+                            tex_name='vu3',
+                            info='Over voltage shadding point 3',
+                            unit='p.u.',
+                            )
+
+        self.TVl1 = NumParam(default=2,
+                             tex_name=r'T_{vl1}',
+                             info='Stand time for (vl2, vl1)',
+                             non_negative=True,
+                             )
+
+        self.TVl2 = NumParam(default=1,
+                             tex_name=r'T_{vl2}',
+                             info='Stand time for (vl3, vl2)',
+                             non_negative=True,
+                             )
+
+        self.TVl3 = NumParam(default=0.16,
+                             tex_name=r'T_{vl3}',
+                             info='Stand time for (vl4, vl3)',
+                             non_negative=True,
+                             )
+
+        self.TVu1 = NumParam(default=1,
+                             tex_name=r'T_{vu1}',
+                             info='Stand time for (vu1, vu2)',
+                             non_negative=True,
+                             )
+
+        self.TVu2 = NumParam(default=0.16,
+                             tex_name=r'T_{vu2}',
+                             info='Stand time for (vu2, vu3)',
+                             non_negative=True,
+                             )
+
+        self.Tres = NumParam(default=0.05,
+                             info='Integrator reset time',
+                             )
+
+
+class DGPRCTBaseModel(Model):
+    """
+    Model implementation of DGPRCT Base.
+    """
+
+    def __init__(self, system, config):
+        Model.__init__(self, system, config)
+        self.flags.tds = True
+        self.group = 'DG'
+
+        self.bus = ExtParam(model='DG', src='bus',
+                            indexer=self.dev,
+                            export=False)
+
+        self.fn = ExtParam(model='DG', src='fn',
+                           indexer=self.dev,
+                           export=False)
+
+        # -- Frequency protection
+        # Convert frequency deviation range to p.u.
+        self.f = ExtAlgeb(export=False,
+                          info='DG frequency read value', unit='p.u.',
+                          model='FreqMeasurement', src='f',
+                          indexer=self.busfreq,
+                          )
+
+        self.fHz = Algeb(v_str='fn * f',
+                         e_str='fn * f - fHz',
+                         info='frequency in Hz',
+                         tex_name=r'f_{Hz}',
+                         )
+
+        # -- Lock DG frequency signal and output power
+
+        self.ltu = ConstService(v_str='0.8')
+        self.ltl = ConstService(v_str='0.2')
+
+        # `Ldsum_zu` is `ue`
+        dsum = 'fen * (IAWfl1_lim_zu * Lfl1_zi + IAWfl2_lim_zu * Lfl2_zi + ' \
+               'IAWfu1_lim_zu * Lfu1_zi + IAWfu2_lim_zu * Lfu2_zi) + ' \
+               'Ven * (IAWVl1_lim_zu * LVl1_zi + IAWVl2_lim_zu * LVl2_zi + ' \
+               'IAWVl3_lim_zu * LVl3_zi + ' \
+               'IAWVu1_lim_zu * LVu1_zi + IAWVu2_lim_zu * LVu2_zi) - ' \
+               'dsum'
+
+        self.dsum = Algeb(v_str='0',
+                          e_str=dsum,
+                          info='lock signal summation',
+                          tex_name=r'd_{tot}',
+                          )
+
+        self.Ldsum = Limiter(u=self.dsum,
+                             lower=self.ltl,
+                             upper=self.ltu,
+                             info='lock signal comparer, zu is to act',
+                             equal=False, no_warn=True,
+                             )
+
+        self.ue = Algeb(v_str='0',
+                        e_str='Ldsum_zu - ue',
+                        info='lock flag',
+                        tex_name=r'ue',
+                        )
+
+        self.zero = ConstService('0')
+
+        self.res = ExtendedEvent(self.ue, t_ext=self.Tres,
+                                 trig="rise",
+                                 extend_only=True)
+
+        # lock DG frequency signal
+
+        # fflag option 1: leave source signal online in protection
+        self.fin = ExtAlgeb(model='DG', src='f',
+                            indexer=self.dev,
+                            info='original f from DG',
+                            )
+
+        self.fHzl = ExtAlgeb(model='DG', src='fHz',
+                             indexer=self.dev,
+                             export=False,
+                             e_str='- ue * (fn * f)',
+                             info='Frequency measure lock',
+                             )
+        # TODO: add fflag option 2: block the source signal in protection
+
+        # lock output power of DG
+
+        self.Pext = ExtAlgeb(model='DG', src='Pext',
+                             indexer=self.dev,
+                             info='original Pext from DG',
+                             )
+
+        self.Pref = ExtAlgeb(model='DG', src='Pref',
+                             indexer=self.dev,
+                             info='original Pref from DG',
+                             )
+
+        self.Pdrp = ExtAlgeb(model='DG', src='DB_y',
+                             indexer=self.dev,
+                             info='original Pdrp from DG',
+                             )
+
+        self.Psum = ExtAlgeb(model='DG', src='Psum',
+                             indexer=self.dev,
+                             export=False,
+                             e_str='- ue * (Pext + Pref + Pdrp)',
+                             info='Active power lock',
+                             )
+
+        self.Qdrp = ExtAlgeb(model='DG', src='Qdrp',
+                             indexer=self.dev,
+                             info='original Qdrp from DG',
+                             )
+
+        self.Qref = ExtAlgeb(model='DG', src='Qref',
+                             indexer=self.dev,
+                             info='original Qref from DG',
+                             )
+
+        self.Qsum = ExtAlgeb(model='DG', src='Qsum',
+                             indexer=self.dev,
+                             export=False,
+                             e_str='- ue * (Qdrp + Qref)',
+                             info='Reactive power lock',
+                             )
+
+        # TODO: apply ExtState here
+        # TODO: clear State: BusFreq WO(Washout)
+        # TODO: clear State: PVD1: Ipout, Iqout
+        # TODO: develop integrator with reset function
+        # TODO: Set EventFlag for ue
+
+
+class fProtect:
+    """
+    Subclass for frequency protection logic.
+    """
+
+    def __init__(self):
+        # Indicatior of frequency deviation
+        self.Lfl1 = Limiter(u=self.fHz,
+                            lower=self.fl3, upper=self.fl1,
+                            info='Frequency comparer for (fl3, fl1)',
+                            equal=False, no_warn=True,
+                            )
+
+        self.Lfl2 = Limiter(u=self.fHz,
+                            lower=self.fl3, upper=self.fl2,
+                            info='Frequency comparer for (fl3, fl2)',
+                            equal=False, no_warn=True,
+                            )
+
+        self.Lfu1 = Limiter(u=self.fHz,
+                            lower=self.fu1, upper=self.fu3,
+                            info='Frequency comparer for (fu1, fu3)',
+                            equal=False, no_warn=True,
+                            )
+
+        self.Lfu2 = Limiter(u=self.fHz,
+                            lower=self.fu2, upper=self.fu3,
+                            info='Frequency comparer for (fu2, fu3)',
+                            equal=False, no_warn=True,
+                            )
+
+        # Frequency deviation time continuity check
+
+        self.IAWfl1 = IntegratorAntiWindup(u='Lfl1_zi * (1 - res) - Tfl1 / Tres * res',
+                                           T=1, K=1, y0='0',
+                                           lower=self.zero, upper=self.Tfl1,
+                                           info='condition check for (fl3, fl1)',
+                                           no_warn=True,
+                                           )
+
+        self.IAWfl2 = IntegratorAntiWindup(u='Lfl2_zi * (1 - res) - Tfl2 / Tres * res',
+                                           T=1, K=1, y0='0',
+                                           lower=self.zero, upper=self.Tfl2,
+                                           info='condition check for (fl3, fl2)',
+                                           no_warn=True,
+                                           )
+
+        self.IAWfu1 = IntegratorAntiWindup(u='Lfu1_zi * (1 - res) - Tfu1 / Tres * res',
+                                           T=1, K=1, y0='0',
+                                           lower=self.zero, upper=self.Tfu1,
+                                           info='condition check for (fu1, fu3)',
+                                           no_warn=True,
+                                           )
+
+        self.IAWfu2 = IntegratorAntiWindup(u='Lfl2_zi * (1 - res) - Tfu2 / Tres * res',
+                                           T=1, K=1, y0='0',
+                                           lower=self.zero, upper=self.Tfu2,
+                                           info='condition check for (fu2, fu3)',
+                                           no_warn=True,
+                                           )
+
+
+class VProtect:
+    """
+    Subclass for voltage protection logic
+    """
+
+    def __init__(self):
+        # -- Voltage protection
+
+        # Indicatior of voltage deviation
+        self.LVl1 = Limiter(u=self.v,
+                            lower=self.vl4, upper=self.vl1,
+                            info='Voltage comparer for (vl4, vl1)',
+                            equal=False, no_warn=True,
+                            )
+
+        self.LVl2 = Limiter(u=self.v,
+                            lower=self.vl4, upper=self.vl2,
+                            info='Voltage comparer for (vl4, vl2)',
+                            equal=False, no_warn=True,
+                            )
+
+        self.LVl3 = Limiter(u=self.v,
+                            lower=self.vl4, upper=self.vl3,
+                            info='Voltage comparer for (vl4, vl3)',
+                            equal=False, no_warn=True,
+                            )
+
+        self.LVu1 = Limiter(u=self.v,
+                            lower=self.vu1, upper=self.vu3,
+                            info='Voltage comparer for (vu1, vu3)',
+                            equal=False, no_warn=True,
+                            )
+
+        self.LVu2 = Limiter(u=self.v,
+                            lower=self.vu2, upper=self.vu3,
+                            info='Voltage comparer for (vu2, vu3)',
+                            equal=False, no_warn=True,
+                            )
+
+        # Voltage deviation time continuity check
+        self.IAWVl1 = IntegratorAntiWindup(u='LVl1_zi * (1 - res) - TVl1 / Tres * res',
+                                           T=1, K=1, y0='0',
+                                           lower=self.zero, upper=self.TVl1,
+                                           info='condition check for (Vl3, Vl1)',
+                                           no_warn=True,
+                                           )
+
+        self.IAWVl2 = IntegratorAntiWindup(u='LVl2_zi * (1 - res) - TVl2 / Tres * res',
+                                           T=1, K=1, y0='0',
+                                           lower=self.zero, upper=self.TVl2,
+                                           info='condition check for (Vl3, Vl2)',
+                                           no_warn=True,
+                                           )
+
+        self.IAWVl3 = IntegratorAntiWindup(u='LVl2_zi * (1 - res) - TVl3 / Tres * res',
+                                           T=1, K=1, y0='0',
+                                           lower=self.zero, upper=self.TVl2,
+                                           info='condition check for (Vl3, Vl2)',
+                                           no_warn=True,
+                                           )
+
+        self.IAWVu1 = IntegratorAntiWindup(u='LVu1_zi * (1 - res) - TVu1 / Tres * res',
+                                           T=1, K=1, y0='0',
+                                           lower=self.zero, upper=self.TVu1,
+                                           info='condition check for (Vu1, Vu3)',
+                                           no_warn=True,
+                                           )
+
+        self.IAWVu2 = IntegratorAntiWindup(u='LVu2_zi * (1 - res) - TVu2 / Tres * res',
+                                           T=1, K=1, y0='0',
+                                           lower=self.zero, upper=self.TVu2,
+                                           info='condition check for (Vu2, Vu3)',
+                                           no_warn=True,
+                                           )
+
+
+class DGPRCT1Model(DGPRCTBaseModel, fProtect, VProtect):
+    """
+    Model implementation of DGPRCT1.
+    """
+
+    def __init__(self, system, config):
+        DGPRCTBaseModel.__init__(self, system, config)
+        self.v = ExtAlgeb(model='Bus', src='v',
+                          indexer=self.bus,
+                          export=False,
+                          info='Bus voltage',
+                          unit='p.u.',
+                          )
+        fProtect.__init__(self)
+        VProtect.__init__(self)
+
+
+class DGPRCT1(DGPRCTBaseData, DGPRCT1Model):
+    """
+    DGPRCT1 model, follow IEEE-1547-2018. DGPRCT stands for DG protection.
+
+    Target device (limited to DG group) ``Psum`` and ``Qsum`` will decrease to zero
+    immediately when frequency/voltage protection flag is raised. Once the lock is
+    released, ``Psum`` and ``Qsum`` will return to normal immediately.
+
+    DG group base model ``PVD1`` already has a degrading function which is used to
+    degrade output under abnormal condition. it is recommended to turn off it by setting
+    `tren = 0`.
+
+    ``fen`` and ``Ven`` are protection enabling parameters. 1/0 is on/off.
+
+    ``ue`` is lock flag signal.
+
+    It should be noted that, the lock only lock the ``fHz`` (frequency read value)
+    of DG model. The source values (which come from ``BusFreq`` `f` remain unchanged.)
+
+    Protection sensors (e.g., IAWfl1) are instances of ``IntergratorAntiWindup``. All
+    the protection sensors will be reset after ``ue`` returns to 0.
+    Resetting takes `Tres` to finish.
+
+    The model does not check the shedding points sequence.
+    The input parameters are required to satisfy ``fl3 < fl2 < fl1 < fu1 < fu2 < fu3``,
+    and ``ul4 < ul3 < ul2 < ul1 < uu1 < uu2 < uu3``.
+
+    Default settings:\n
+    Frequency (Hz):\n
+    `(fl3, fl2), Tfl2` [(50.0, 57.5), 10s]\n
+    `(fl2, fl1), Tfl1` [(57.5, 59.2), 300s]\n
+    `(fu1, fu2), Tfu1` [(60.5, 61.5), 300s]\n
+    `(fu2, fu3), Tfu2` [(61.5, 70.0), 10s]\n
+
+    Voltage (p.u.):\n
+    `(vl4, vl3), TVl3` [(0.10, 0.45), 0.16s]\n
+    `(vl3, vl2), TVl2` [(0.45, 0.60), 1s]\n
+    `(vl2, vl1), TVl1` [(0.60, 0.88), 2s]\n
+    `(vu1, vu2), TVu1` [(1.10, 1.20), 1s]\n
+    `(vu2, vu3), TVu2` [(1.20, 2.00), 0.16s]\n
+
+    Reference:
+
+    NERC. Bulk Power System Reliability Perspectives on the Adoption of IEEE 1547-2018.
+    March 2020. Available:
+
+    https://www.nerc.com/comm/PC_Reliability_Guidelines_DL/Guideline_IEEE_1547-2018_BPS_Perspectives.pdf
+    """
+
+    def __init__(self, system, config):
+        DGPRCTBaseData.__init__(self)
+        DGPRCT1Model.__init__(self, system, config)
+
+
+class DGPRCTExtModel(DGPRCTBaseModel, fProtect, VProtect):
+    """
+    Model implementation of DGPRCT1.
+    """
+
+    def __init__(self, system, config):
+        DGPRCTBaseModel.__init__(self, system, config)
+
+        self.v = ExtService(model='Bus', src='v',
+                            indexer=self.bus,
+                            info='retrived voltage',
+                            tex_name='v',
+                            )
+
+        fProtect.__init__(self)
+        VProtect.__init__(self)
+
+
+class DGPRCTExt(DGPRCTBaseData, DGPRCTExtModel):
+    """
+    DGPRCT External model, follow IEEE-1547. DGPRCT stands for DG protection.
+
+    Similar to DGPRCT1, but the measured voltage is retrived from outside.
+
+    DG group base model ``PVD1`` already has a degrading function which is used to
+    degrade output under abnormal condition. it is recommended to turn off it by setting
+    `tren = 0`.
+
+    Target device (limited to DG group) ``Psum`` and ``Qsum`` will decrease to zero
+    immediately when frequency/voltage protection flag is raised. Once the lock is
+    released, ``Psum`` and ``Qsum`` will return to normal immediately.
+
+    ``fen`` and ``Ven`` are protection enabling parameters. 1/0 is on/off.
+
+    ``ue`` is lock flag signal.
+
+    It should be noted that, the lock only lock the ``fHz`` (frequency read value)
+    of DG model. The source values (which come from ``BusFreq`` `f` remain unchanged.)
+
+    Protection sensors (like IAWfl1) are realized by ``IntergratorAntiWindup``. All
+    the protection sensors will be reset after ``ue`` return to 0. Reset action takes
+    `Tres` to finish.
+
+    The model does not check the shedding points sequence.
+    The input parameters are required to satisfy `fl3 < fl2 < fl1 < fu1 < fu2 < fu3`,
+    and `ul4 < ul3 < ul2 < ul1 < uu1 < uu2 < uu3`.
+
+    Default settings:\n
+    Frequency (Hz):\n
+    `(fl3, fl2), Tfl2` [(50.0, 57.5), 10s]\n
+    `(fl2, fl1), Tfl1` [(57.5, 59.2), 300s]\n
+    `(fu1, fu2), Tfu1` [(60.5, 61.5), 300s]\n
+    `(fu2, fu3), Tfu2` [(61.5, 70.0), 10s]\n
+
+    Voltage (p.u.):\n
+    `(vl4, vl3), TVl3` [(0.10, 0.45), 0.16s]\n
+    `(vl3, vl2), TVl2` [(0.45, 0.60), 1s]\n
+    `(vl2, vl1), TVl1` [(0.60, 0.88), 2s]\n
+    `(vu1, vu2), TVu1` [(1.10, 1.20), 1s]\n
+    `(vu2, vu3), TVu2` [(1.20, 2.00), 0.16s]\n
+    """
+
+    def __init__(self, system, config):
+        DGPRCTBaseData.__init__(self)
+        DGPRCTExtModel.__init__(self, system, config)
diff --git a/andes/models/distributed/plk.py b/andes/models/distributed/plk.py
deleted file mode 100644
index 8741b6d13..000000000
--- a/andes/models/distributed/plk.py
+++ /dev/null
@@ -1,184 +0,0 @@
-"""
-DER protection model.
-"""
-from andes.core.param import IdxParam, NumParam, ExtParam
-from andes.core.model import Model, ModelData
-from andes.core.var import Algeb, ExtAlgeb
-from andes.core.service import ConstService
-from andes.core.discrete import Limiter, Delay
-
-
-class PLKData(ModelData):
-    """
-    Plock model data.
-    """
-
-    def __init__(self):
-        super(PLKData, self).__init__()
-        self.dev = IdxParam(info='idx of the target device',
-                            mandatory=True,
-                            )
-        self.busfreq = IdxParam(model='BusFreq',
-                                info='Target device interface bus measurement device idx',
-                                )
-
-        # -- protection enable parameters
-        self.fena = NumParam(default=1,
-                             tex_name='fena',
-                             vrange=(0, 1),
-                             info='Frequency deviation protection enable. \
-                                   1 for enable, 0 for disable.',
-                             )
-        self.Vena = NumParam(default=0,
-                             tex_name='Vena',
-                             vrange=(0, 1),
-                             info='Voltage deviation protection enable.\
-                                   1 for enable, 0 for disable.',
-                             )
-
-        # -- protection parameters
-        self.fl = NumParam(default=57.5,
-                           tex_name='f_l',
-                           info='Under frequency shadding point',
-                           unit='Hz',
-                           )
-        self.fu = NumParam(default=61.5,
-                           tex_name='f_u',
-                           info='Over frequency shadding point',
-                           unit='Hz',
-                           )
-        self.vl = NumParam(default=0.88,
-                           tex_name='V_l',
-                           info='Under voltage shadding point',
-                           unit='p.u.',
-                           )
-        self.vu = NumParam(default=1.1,
-                           tex_name='V_u',
-                           info='Over voltage shadding point',
-                           unit='p.u.',
-                           )
-        self.Tf = NumParam(default=10,
-                           tex_name=r't_{fdev}',
-                           info='Stand time under frequency deviation',
-                           non_negative=True,
-                           )
-        self.Tv = NumParam(default=1,
-                           tex_name=r't_{udev}',
-                           info='Stand time under voltage deviation',
-                           non_negative=True,
-                           )
-
-
-class PLKModel(Model):
-    """
-    Model implementation of Plock.
-    """
-
-    def __init__(self, system, config):
-        Model.__init__(self, system, config)
-        self.flags.tds = True
-        self.group = 'DG'
-
-        self.bus = ExtParam(model='DG',
-                            src='bus',
-                            indexer=self.dev,
-                            export=False)
-
-        # -- Voltage protection
-        self.v = ExtAlgeb(model='Bus',
-                          src='v',
-                          indexer=self.bus,
-                          export=False,
-                          info='Bus voltage',
-                          unit='p.u.',
-                          )
-        # Indicatior of voltage deviation
-        self.Vcmp = Limiter(u=self.v,
-                            lower=self.vl,
-                            upper=self.vu,
-                            tex_name=r'V_{cmp}',
-                            info='Voltage comparator',
-                            equal=False,
-                            )
-        self.Volt_dev = Algeb(v_str='0',
-                              e_str='1 - Vcmp_zi - Volt_dev',
-                              info='Voltage deviation indicator',
-                              tex_name='zs_{Vdev}',
-                              )
-        # Delayed voltage deviation indicator
-        self.Volt_devd = Delay(u=self.Volt_dev,
-                               mode='time',
-                               delay=self.Tv.v)
-
-        # -- Frequency protection
-        self.fn = ExtParam(model='DG',
-                           src='fn',
-                           indexer=self.dev,
-                           export=False)
-        # Convert frequency deviation range to p.u.
-        self.fln = ConstService(tex_name=r'fl_{n}',
-                                v_str='fl/fn',
-                                )
-        self.fun = ConstService(tex_name=r'fu_{n}',
-                                v_str='fu/fn',
-                                )
-        self.f = ExtAlgeb(model='FreqMeasurement',
-                          src='f',
-                          indexer=self.busfreq,
-                          export=False,
-                          info='Bus frequency',
-                          unit='p.u.',
-                          )
-
-        # Indicatior of frequency deviation
-        self.fcmp = Limiter(u=self.f,
-                            lower=self.fln,
-                            upper=self.fun,
-                            tex_name=r'f_{cmp}',
-                            info='Frequency comparator',
-                            equal=False,
-                            )
-        self.freq_dev = Algeb(v_str='0',
-                              e_str='1 - fcmp_zi - freq_dev',
-                              info='Frequency deviation indicator',
-                              tex_name='zs_{Fdev}',
-                              )
-        # Delayed frequency deviation indicator
-        self.freq_devd = Delay(u=self.freq_dev,
-                               mode='time',
-                               delay=self.Tf.v)
-
-        # -- Lock PVD1 current command
-        # freqyency protection
-        self.Ipul_f = ExtAlgeb(model='DG',
-                               src='Ipul',
-                               indexer=self.dev,
-                               export=False,
-                               e_str='-1000 * Ipul_f * freq_devd_v * freq_dev * fena',
-                               info='Current locker from frequency protection',
-                               )
-        # voltage protection
-        self.Ipul_V = ExtAlgeb(model='DG',
-                               src='Ipul',
-                               indexer=self.dev,
-                               export=False,
-                               e_str='-1000 * Ipul_V * Volt_devd_v * Volt_dev * Vena',
-                               info='Current locker from voltage protection',
-                               )
-
-
-class PLK(PLKData, PLKModel):
-    """
-    DER protection model. PLK stands for Power Lock.
-
-    Target device (limited to DG group) ``Ipul`` will drop to zero immediately
-    when frequency/voltage protection is triggered.
-
-    Once the lock is released, ``Ipul`` will return to normal immediately.
-
-    ``fena`` and ``Vena`` are protection enabling parameters. 1 is on and 0 is off.
-    """
-
-    def __init__(self, system, config):
-        PLKData.__init__(self)
-        PLKModel.__init__(self, system, config)
diff --git a/andes/models/distributed/pvd1.py b/andes/models/distributed/pvd1.py
index f400a32dd..90cbafbfa 100644
--- a/andes/models/distributed/pvd1.py
+++ b/andes/models/distributed/pvd1.py
@@ -199,6 +199,11 @@ def __init__(self):
                                vrange='(0, 1]',
                                )
 
+        self.recflag = NumParam(default=1, tex_name=r'z_{rec}',
+                                info='Enable flag for voltage and frequency recovery limiters',
+                                vrange='(0, 1)',
+                                )
+
 
 class PVD1Model(Model):
     """
@@ -407,15 +412,27 @@ def __init__(self, system, config):
                            no_warn=True,
                            )
 
-        Qsum = 'u * VQ1_zl * qmx + VQ2_zu * qmn + ' \
+        Qdrp = 'u * VQ1_zl * qmx + VQ2_zu * qmn + ' \
                'u * VQ1_zi * (qmx + dqdv *(Vqu - Vcomp)) + ' \
-               'u * VQ2_zi * (dqdv * (v1 - Vcomp)) + ' \
-               'u * qref0'
+               'u * VQ2_zi * (dqdv * (v1 - Vcomp)) '
+
+        self.Qdrp = Algeb(tex_name='Q_{drp}',
+                          info='External power signal (for AGC)',
+                          v_str=Qdrp,
+                          e_str=f'{Qdrp} - Qdrp',
+                          discrete=(self.VQ1, self.VQ2),
+                          )
 
-        self.Qsum = Algeb(info='Total Q (droop + initial)',
-                          v_str=Qsum,
-                          e_str=f'{Qsum} - Qsum',
-                          tex_name='Q_{sum}',
+        self.Qref = Algeb(tex_name=r'Q_{ref}',
+                          info='Reference power signal (for scheduling setpoint)',
+                          v_str='u * qref0',
+                          e_str='u * qref0 - Qref'
+                          )
+
+        self.Qsum = Algeb(tex_name=r'Q_{tot}',
+                          info='Sum of Q signals',
+                          v_str=f'u * (qref0 + {Qdrp})',
+                          e_str='u * (Qref + Qdrp) - Qsum',
                           discrete=(self.VQ1, self.VQ2),
                           )
 
@@ -452,16 +469,17 @@ def __init__(self, system, config):
                            tex_name='I_{qmax}',
                            )
 
+        # TODO: set option whether to use degrading gain
         # --- `Ipcmd` and `Iqcmd` ---
         self.Ipcmd = GainLimiter(u=self.Ipul,
-                                 K=1, R='Fvl * Fvh * Ffl * Ffh',
+                                 K=1, R='Fvl * Fvh * Ffl * Ffh * recflag + 1 * (1 - recflag)',
                                  lower=0, upper=self.Ipmax,
                                  info='Ip with limiter and coeff.',
                                  tex_name='I^{pcmd}',
                                  )
 
         self.Iqcmd = GainLimiter(u=self.Iqul,
-                                 K=1, R='Fvl * Fvh * Ffl * Ffh',
+                                 K=1, R='Fvl * Fvh * Ffl * Ffh * recflag + 1 * (1 - recflag)',
                                  lower=self.Iqmax, sign_lower=-1,
                                  upper=self.Iqmax,
                                  info='Iq with limiter and coeff.',
@@ -476,15 +494,6 @@ def __init__(self, system, config):
                          info='Output Iq filter',
                          )
 
-        self.Pe = Algeb(tex_name='P_e',
-                        info='active power output',
-                        e_str='v * Ipout_y - Pe',
-                        v_str='pref0')
-        self.Qe = Algeb(tex_name='Q_e',
-                        info='reactive power output',
-                        e_str='v * Iqout_y - Qe',
-                        v_str='qref0')
-
     def v_numeric(self, **kwargs):
         """
         Disable the corresponding `StaticGen`s.
diff --git a/andes/models/group.py b/andes/models/group.py
index a2f1f048f..12b0c20cc 100644
--- a/andes/models/group.py
+++ b/andes/models/group.py
@@ -107,13 +107,7 @@ def idx2model(self, idx, allow_none=False):
         """
 
         ret = []
-        single = False
-
-        if not isinstance(idx, (list, tuple, np.ndarray)):
-            single = True
-            idx = (idx,)
-        elif len(idx) > 0 and isinstance(idx[0], (list, tuple, np.ndarray)):
-            idx = list_flatten(idx)
+        idx, single = self._vectorize_idx(idx)
 
         for i in idx:
             try:
@@ -179,11 +173,7 @@ def get(self, src: str, idx, attr: str = 'v', allow_none=False, default=0.0):
         """
         self._check_src(src)
         self._check_idx(idx)
-
-        single = False
-        if not isinstance(idx, (list, np.ndarray)):
-            idx = [idx]
-            single = True
+        idx, single = self._vectorize_idx(idx)
 
         n = len(idx)
         if n == 0:
@@ -245,9 +235,6 @@ def set(self, src: str, idx, attr, value):
         self._check_src(src)
         self._check_idx(idx)
 
-        if not isinstance(idx, (list, np.ndarray)):
-            idx = [idx]
-
         if isinstance(value, (float, str, int)):
             value = [value] * len(idx)
 
@@ -288,15 +275,73 @@ def find_idx(self, keys, values, allow_none=False, default=None):
         return out
 
     def _check_src(self, src: str):
+        """
+        Helper function for checking if ``src`` is a shared field.
+
+        The requirement is not strictly enforced and is only for debugging purposed.
+        """
         if src not in self.common_vars + self.common_params:
-            # raise AttributeError(f'Group <{self.class_name}> does not share property <{src}>.')
             logger.debug(f'Group <{self.class_name}> does not share property <{src}>.')
-            pass
 
     def _check_idx(self, idx):
+        """
+        Helper function for checking if ``idx`` is None.
+
+        Raises IndexError if idx is None.
+        """
+
         if idx is None:
             raise IndexError(f'{self.class_name}: idx cannot be None')
 
+    def _vectorize_idx(self, idx):
+        """
+        Helper function to convert idx into a list if the input is
+        a single element.
+
+        If the input is a nested list, flatten it into a 1-dimensional
+        list.
+
+        Returns
+        -------
+        idx : list
+            List of indices.
+        single : bool
+            True if the input is a single element.
+        """
+        single = False
+
+        if not isinstance(idx, (list, np.ndarray)):
+            idx = [idx]
+            single = True
+        elif len(idx) > 0 and isinstance(idx[0], (list, tuple, np.ndarray)):
+            idx = list_flatten(idx)
+
+        return idx, single
+
+    def get_field(self, src: str, idx, field: str):
+        """
+        Helper function for retrieving an attribute of a member variable shared
+        by models in this group.
+
+        Returns
+        -------
+        list
+            A list with the length equal to ``len(idx)``.
+        """
+
+        self._check_src(src)
+        self._check_idx(idx)
+        idx, _ = self._vectorize_idx(idx)
+
+        models = self.idx2model(idx, allow_none=True)
+
+        ret = [None] * self.n
+        for ii, model in enumerate(models):
+            if model is not None:
+                ret[ii] = getattr(model.__dict__[src], field)
+
+        return ret
+
     def get_next_idx(self, idx=None, model_name=None):
         """
         Get a no-conflict idx for a new device.
diff --git a/andes/models/measurement.py b/andes/models/measurement.py
index e6540c33b..46a238e9a 100644
--- a/andes/models/measurement.py
+++ b/andes/models/measurement.py
@@ -13,9 +13,10 @@
 
 class BusFreq(ModelData, Model):
     """
-    Bus frequency measurement.
+    Bus frequency measurement. Outputs frequency in per unit value.
 
-    Bus frequency output variable is `f`.
+    The bus frequency output variable is `f`.
+    The frequency deviation variable is `WO_y`.
     """
     def __init__(self, system, config):
         ModelData.__init__(self)
@@ -25,12 +26,22 @@ def __init__(self, system, config):
 
         # Parameters
         self.bus = IdxParam(info="bus idx", mandatory=True)
-        self.Tf = NumParam(default=0.02, info="input digital filter time const", unit="sec",
-                           tex_name='T_f')
-        self.Tw = NumParam(default=0.02, info="washout time const", unit="sec",
-                           tex_name='T_w')
-        self.fn = NumParam(default=60.0, info="nominal frequency", unit='Hz',
-                           tex_name='f_n')
+
+        self.Tf = NumParam(default=0.02,
+                           info="input digital filter time const",
+                           unit="sec",
+                           tex_name='T_f',
+                           )
+        self.Tw = NumParam(default=0.02,
+                           info="washout time const",
+                           unit="sec",
+                           tex_name='T_w',
+                           )
+        self.fn = NumParam(default=60.0,
+                           info="nominal frequency",
+                           unit='Hz',
+                           tex_name='f_n',
+                           )
 
         # Variables
         self.iwn = ConstService(v_str='u / (2 * pi * fn)', tex_name=r'1/\omega_n')
diff --git a/andes/models/renewable/__init__.py b/andes/models/renewable/__init__.py
index fa88787c8..82e808112 100644
--- a/andes/models/renewable/__init__.py
+++ b/andes/models/renewable/__init__.py
@@ -4,7 +4,7 @@
 
 from andes.models.renewable.regca1 import REGCA1      # NOQA
 from andes.models.renewable.reeca1 import REECA1      # NOQA
-from andes.models.renewable.reeca1e import REECA1E    # NOQA
+from andes.models.renewable.reeca1e import REECA1E, REECA1G    # NOQA
 from andes.models.renewable.repca1 import REPCA1      # NOQA
 from andes.models.renewable.wtara1 import WTARA1      # NOQA
 from andes.models.renewable.wtarv1 import WTARV1      # NOQA
diff --git a/andes/models/renewable/reeca1e.py b/andes/models/renewable/reeca1e.py
index 37313344e..27a5ffe8f 100644
--- a/andes/models/renewable/reeca1e.py
+++ b/andes/models/renewable/reeca1e.py
@@ -3,7 +3,7 @@
 """
 
 from andes.core.param import NumParam, IdxParam
-from andes.core.var import ExtAlgeb
+from andes.core.var import ExtAlgeb, ExtState
 from andes.core.service import DeviceFinder
 
 from andes.models.renewable.reeca1 import REECA1Data, REECA1Model
@@ -13,12 +13,19 @@ class REECA1EData(REECA1Data):
     """
     Data for REECA1E.
     """
+
     def __init__(self):
         REECA1Data.__init__(self)
+        self.Kf = NumParam(default=0.0,
+                           info='gain for frequency deviation',
+                           tex_name='K_{df}',
+                           )
+
         self.Kdf = NumParam(default=0.0,
-                            info='gain for frequency derivative',
+                            info='gain for rate-of-change of frequency',
                             tex_name='K_{df}',
                             )
+
         self.busroc = IdxParam(info='Optional BusROCOF device idx',
                                model='BusROCOF',
                                default=None,
@@ -29,6 +36,7 @@ class REECA1EModel(REECA1Model):
     """
     Model for REECA1E.
     """
+
     def __init__(self, system, config):
         REECA1Model.__init__(self, system, config)
         self.busrocof = DeviceFinder(self.busroc,
@@ -37,22 +45,83 @@ def __init__(self, system, config):
                                      )
 
         self.df = ExtAlgeb(model='FreqMeasurement',
-                           src='Wf_y',
+                           src='WO_y',
                            indexer=self.busrocof,
                            export=False,
-                           info='Bus ROCOF',
-                           unit='p.u.',
+                           info='Bus frequency deviation',
                            )
 
-        self.Pref.e_str += '- Kdf * df'
+        self.dfdt = ExtAlgeb(model='FreqMeasurement',
+                             src='Wf_y',
+                             indexer=self.busrocof,
+                             export=False,
+                             info='Bus ROCOF',
+                             unit='p.u.',
+                             )
+
+        self.Pref.e_str += '- Kdf * dfdt - Kf * df'
 
 
 class REECA1E(REECA1EData, REECA1EModel):
     """
-    REGCA1 with inertia emulation.
+    REGCA1 with inertia emulation and primary frequency droop.
+    Measurements are based on frequency measurement model.
 
-    Bus ROCOF obtained from `BusROCOF` devices.
+    Bus ROCOF obtained from ``BusROCOF`` devices.
     """
+
     def __init__(self, system, config):
         REECA1EData.__init__(self)
         REECA1EModel.__init__(self, system, config)
+
+
+class REECA1GData(REECA1Data):
+    """
+    Data for REECA1G.
+    """
+
+    def __init__(self):
+        REECA1Data.__init__(self)
+        self.Kf = NumParam(default=0.0,
+                           info='gain for frequency deviation',
+                           tex_name='K_{df}',
+                           )
+
+        self.sg = IdxParam(info='synchronous gen idx',
+                           model='Synchronous',
+                           default=None,
+                           mandatory=True,
+                           )
+
+
+class REECA1GModel(REECA1Model):
+    """
+    Model for REECA1G. See docstring for REECA1G.
+    """
+
+    def __init__(self, system, config):
+        REECA1Model.__init__(self, system, config)
+
+        self.omega = ExtState(model='SynGen',
+                              src='omega',
+                              indexer=self.sg,
+                              export=False,
+                              info='generator speed',
+                              unit='pu',
+                              )
+
+        self.Pref.e_str += '- Kf * (omega - 1)'
+
+
+class REECA1G(REECA1GData, REECA1GModel):
+    """
+    REECA1G is a variant of REECA1E.
+
+    REECA1G uses speed from synchronous generators.
+
+    The application of this model is limited because it is uncommon
+    to connect a SynGen on the same bus as a RenGen.
+    """
+    def __init__(self, system, config):
+        REECA1GData.__init__(self)
+        REECA1GModel.__init__(self, system, config)
diff --git a/andes/models/renewable/regca1.py b/andes/models/renewable/regca1.py
index 9818b6dc6..1c30b7369 100644
--- a/andes/models/renewable/regca1.py
+++ b/andes/models/renewable/regca1.py
@@ -1,3 +1,7 @@
+"""
+Module for renewable energy generator (converter) model A.
+"""
+
 from andes.core import ModelData, IdxParam, NumParam, Model, ExtAlgeb, ExtService, ConstService, ExtParam, Algeb, \
     Piecewise, Lag
 from andes.core.block import LagAntiWindupRate, GainLimiter
@@ -268,6 +272,12 @@ def v_numeric(self, **kwargs):
 
 
 class REGCA1(REGCA1Data, REGCA1Model):
+    """
+    Renewable energy generator model type A.
+
+    Implements ``REGCA1`` in PSS/E, or ``REGC_A`` in PSLF.
+    """
+
     def __init__(self, system, config):
         REGCA1Data.__init__(self)
         REGCA1Model.__init__(self, system, config)
diff --git a/andes/models/renewable/regcvsg.py b/andes/models/renewable/regcvsg.py
index 60bf13a7a..b17ffcccb 100644
--- a/andes/models/renewable/regcvsg.py
+++ b/andes/models/renewable/regcvsg.py
@@ -90,26 +90,26 @@ class VSGOuterPIData:
     """
 
     def __init__(self) -> None:
-        self.kp_dv = NumParam(default=20, tex_name=r'kp_{dv}',
-                              info='vd controller proportional gain',
-                              unit='p.u.',
-                              power=True,
-                              )
-        self.ki_dv = NumParam(default=0.001, tex_name=r'ki_{dv}',
-                              info='vd controller integral gain',
-                              unit='p.u.',
-                              power=True,
-                              )
-        self.kp_qv = NumParam(default=20, tex_name=r'kp_{qv}',
-                              info='vq controller proportional gain',
-                              unit='p.u.',
-                              power=True,
-                              )
-        self.ki_qv = NumParam(default=0.001, tex_name=r'ki_{qv}',
-                              info='vq controller integral gain',
-                              unit='p.u.',
-                              power=True,
-                              )
+        self.Kpvd = NumParam(default=20, tex_name=r'kp_{vd}',
+                             info='vd controller proportional gain',
+                             unit='p.u.',
+                             power=True,
+                             )
+        self.Kivd = NumParam(default=0.001, tex_name=r'ki_{vd}',
+                             info='vd controller integral gain',
+                             unit='p.u.',
+                             power=True,
+                             )
+        self.Kpvq = NumParam(default=20, tex_name=r'kp_{vq}',
+                             info='vq controller proportional gain',
+                             unit='p.u.',
+                             power=True,
+                             )
+        self.Kivq = NumParam(default=0.001, tex_name=r'ki_{vq}',
+                             info='vq controller integral gain',
+                             unit='p.u.',
+                             power=True,
+                             )
 
 
 class VSGInnerPIData:
@@ -118,26 +118,26 @@ class VSGInnerPIData:
     """
 
     def __init__(self):
-        self.kp_di = NumParam(default=500, tex_name=r'kp_{di}',
-                              info='Id controller proportional gain',
-                              unit='p.u.',
-                              power=True,
-                              )
-        self.ki_di = NumParam(default=0.2, tex_name=r'ki_{di}',
-                              info='Id controller integral gain',
-                              unit='p.u.',
-                              power=True,
-                              )
-        self.kp_qi = NumParam(default=500, tex_name=r'kp_{qi}',
-                              info='Iq controller proportional gain',
-                              unit='p.u.',
-                              power=True,
-                              )
-        self.ki_qi = NumParam(default=0.2, tex_name=r'ki_{qi}',
-                              info='Iq controller integral gain',
-                              unit='p.u.',
-                              power=True,
-                              )
+        self.KpId = NumParam(default=500, tex_name=r'kp_{di}',
+                             info='Id controller proportional gain',
+                             unit='p.u.',
+                             power=True,
+                             )
+        self.KiId = NumParam(default=0.2, tex_name=r'ki_{di}',
+                             info='Id controller integral gain',
+                             unit='p.u.',
+                             power=True,
+                             )
+        self.KpIq = NumParam(default=500, tex_name=r'kp_{qi}',
+                             info='Iq controller proportional gain',
+                             unit='p.u.',
+                             power=True,
+                             )
+        self.KiIq = NumParam(default=0.2, tex_name=r'ki_{qi}',
+                             info='Iq controller integral gain',
+                             unit='p.u.',
+                             power=True,
+                             )
 
 
 class REGCVSGModelBase(Model):
@@ -282,19 +282,19 @@ class VSGOuterPIModel:
     """
 
     def __init__(self):
-        self.PIdv = PIController(u='vref2 - vd',
-                                 kp=self.kp_dv,
-                                 ki=self.ki_dv,
+        self.PIvd = PIController(u='vref2 - vd',
+                                 kp=self.Kpvd,
+                                 ki=self.Kivd,
                                  x0='Id0',
                                  )
-        self.PIqv = PIController(u='- vq',
-                                 kp=self.kp_qv,
-                                 ki=self.ki_qv,
+        self.PIvq = PIController(u='vq',
+                                 kp=self.Kpvq,
+                                 ki=self.Kivq,
                                  x0='Iq0',
                                  )
 
-        self.Idref = AliasAlgeb(self.PIdv_y)
-        self.Iqref = AliasAlgeb(self.PIqv_y)
+        self.Idref = AliasAlgeb(self.PIvd_y)
+        self.Iqref = AliasAlgeb(self.PIvq_y)
 
 
 class VSGInnerPIModel:
@@ -304,35 +304,35 @@ class VSGInnerPIModel:
 
     def __init__(self):
         self.udref0 = ConstService(tex_name=r'u_{dref0}',
-                                   v_str='ra * Id0 - xs * Iq0 + vd0'
+                                   v_str='vd0 + ra*Id0 - xs*Iq0'
                                    )
         self.uqref0 = ConstService(tex_name=r'u_{qref0}',
-                                   v_str='ra * Iq0 + xs * Id0 + vq0',
+                                   v_str='vq0 + ra*Iq0 + xs*Id0',
                                    )
 
-        # PIdv_y, PIqv_y are Idref, Iqref
-        self.PIdi = PIController(u='PIdv_y - Id',
-                                 kp=self.kp_di,
-                                 ki=self.ki_di,
+        # PIvd_y, PIvq_y are Idref, Iqref
+        self.PIId = PIController(u='PIvd_y - Id',
+                                 kp=self.KpId,
+                                 ki=self.KiId,
                                  )
-        self.PIqi = PIController(u='PIqv_y - Iq',
-                                 kp=self.kp_qi,
-                                 ki=self.ki_qi,
+        self.PIIq = PIController(u='PIvq_y - Iq',
+                                 kp=self.KpIq,
+                                 ki=self.KiIq,
                                  )
 
         # udLag_y, uqLag_y are ud, uq
-        self.Id.e_str = '- ra * Id  + (udLag_y - vd) + Iq * xs'
-        self.Iq.e_str = '- ra * Iq  + (uqLag_y - vq) - Id * xs'
+        self.Id.e_str = 'vd + ra*Id - xs*Iq - udLag_y'
+        self.Iq.e_str = 'vq + ra*Iq + xs*Id - uqLag_y'
 
         self.udref = Algeb(tex_name=r'u_{dref}',
                            info='ud reference',
                            v_str='udref0',
-                           e_str='PIdi_y + vd - Iq * xs - udref',
+                           e_str='PIId_y + vd - Iqref * xs - udref',
                            )
         self.uqref = Algeb(tex_name=r'u_{qref}',
                            info='uq reference',
                            v_str='uqref0',
-                           e_str='PIqi_y + vq + Id * xs - uqref',
+                           e_str='PIIq_y + vq + Idref * xs - uqref',
                            )
 
         self.udLag = Lag(u='udref',
diff --git a/andes/models/renewable/regcvsg2.py b/andes/models/renewable/regcvsg2.py
index 274f918be..50d1bdd74 100644
--- a/andes/models/renewable/regcvsg2.py
+++ b/andes/models/renewable/regcvsg2.py
@@ -16,8 +16,8 @@ class VSGInnerLagModel:
     REGCVSG2 model with lag transfer functions replacing PI controllers.
     """
     def __init__(self):
-        self.LGId = Lag(u=self.PIdv_y, T=self.Tid, K=-1)  # Id
-        self.LGIq = Lag(u=self.PIqv_y, T=self.Tiq, K=-1)  # Iq
+        self.LGId = Lag(u=self.PIvd_y, T=self.Tid, K=-1)  # Id
+        self.LGIq = Lag(u=self.PIvq_y, T=self.Tiq, K=-1)  # Iq
 
         self.Id.e_str = 'LGId_y - Id'
         self.Iq.e_str = 'LGIq_y - Iq'
diff --git a/andes/models/timer.py b/andes/models/timer.py
index 9ca8f8994..3fce21240 100644
--- a/andes/models/timer.py
+++ b/andes/models/timer.py
@@ -128,11 +128,9 @@ def __init__(self, system, config):
 
         self.gf = ConstService(tex_name='g_{f}',
                                v_str='re(1/(rf + 1j * xf))',
-                               vtype=complex,
                                )
         self.bf = ConstService(tex_name='b_{f}',
                                v_str='im(1/(rf + 1j * xf))',
-                               vtype=complex,
                                )
 
         # uf: an internal flag of whether the fault is in action (1) or not (0)
diff --git a/andes/models/vsc.py b/andes/models/vsc.py
index bb2f99174..06befb3ee 100644
--- a/andes/models/vsc.py
+++ b/andes/models/vsc.py
@@ -39,8 +39,12 @@ def __init__(self, system, config):
         self.flags.update({'pflow': True})
         self.group = 'StaticACDC'
 
-        self.gsh = ConstService(tex_name='g_{sh}', v_str='re(1/(rsh + 1j * xsh))', vtype=complex)
-        self.bsh = ConstService(tex_name='b_{sh}', v_str='im(1/(rsh + 1j * xsh))', vtype=complex)
+        self.gsh = ConstService(tex_name='g_{sh}',
+                                v_str='re(1/(rsh + 1j * xsh))',
+                                )
+        self.bsh = ConstService(tex_name='b_{sh}',
+                                v_str='im(1/(rsh + 1j * xsh))',
+                                )
 
         self.mode = Switcher(u=self.control, options=(0, 1, 2, 3))
 
diff --git a/andes/plot.py b/andes/plot.py
index c6376d33c..5079f787c 100644
--- a/andes/plot.py
+++ b/andes/plot.py
@@ -746,15 +746,18 @@ def panoview(self, mdl, *, ncols=3, vars=None, idx=None, a=None, figsize=None, *
         states = list()
         algebs = list()
 
+        states_and_ext = mdl.cache.states_and_ext
+        algebs_and_ext = mdl.cache.algebs_and_ext
+
         if vars is None:
-            states = mdl.states.values()
-            algebs = mdl.algebs.values()
+            states = states_and_ext.values()
+            algebs = algebs_and_ext.values()
         else:
             for item in vars:
-                if item in mdl.states:
-                    states.append(mdl.states[item])
-                elif item in mdl.algebs:
-                    algebs.append(mdl.algebs[item])
+                if item in states_and_ext:
+                    states.append(states_and_ext[item])
+                elif item in algebs_and_ext:
+                    algebs.append(algebs_and_ext[item])
                 else:
                     logger.warning("Variable <%s> does not exist in model <%s>",
                                    item, mdl.class_name)
diff --git a/andes/routines/tds.py b/andes/routines/tds.py
index 333a9f155..c23945b67 100644
--- a/andes/routines/tds.py
+++ b/andes/routines/tds.py
@@ -696,13 +696,18 @@ def test_init(self):
             return True
 
         # otherwise, show suspect initialization error
-        fail_idx = np.where(abs(system.dae.fg) >= self.config.tol)
-        fail_names = [system.dae.xy_name[int(i)] for i in np.ravel(fail_idx)]
+        fail_idx = np.ravel(np.where(abs(system.dae.fg) >= self.config.tol))
+        nan_idx = np.ravel(np.where(np.isnan(system.dae.fg)))
+        bad_idx = np.hstack([fail_idx, nan_idx])
+
+        fail_names = [system.dae.xy_name[int(i)] for i in fail_idx]
+        nan_names = [system.dae.xy_name[int(i)] for i in nan_idx]
+        bad_names = fail_names + nan_names
 
         title = 'Suspect initialization issue! Simulation may crash!'
-        err_data = {'Name': fail_names,
-                    'Var. Value': system.dae.xy[fail_idx],
-                    'Eqn. Mismatch': system.dae.fg[fail_idx],
+        err_data = {'Name': bad_names,
+                    'Var. Value': system.dae.xy[bad_idx],
+                    'Eqn. Mismatch': system.dae.fg[bad_idx],
                     }
         tab = Tab(title=title,
                   header=err_data.keys(),
diff --git a/andes/variables/dae.py b/andes/variables/dae.py
index 8d92df96b..2c9f2c57e 100644
--- a/andes/variables/dae.py
+++ b/andes/variables/dae.py
@@ -134,7 +134,7 @@ def get_data(self, base_vars: Union[BaseVar, List[BaseVar]], a=None):
         base_var : BaseVar or a sequence of BaseVar(s)
             The variable types and internal addresses
             are used for looking up the data.
-        a : an array-like of int or None
+        a : an array/list of int or None
             Sub-indices into the address of `base_var`.
             Applied to each variable.
 
diff --git a/docs/requirements-rtd.txt b/docs/requirements-rtd.txt
index dba435249..84bbd0603 100644
--- a/docs/requirements-rtd.txt
+++ b/docs/requirements-rtd.txt
@@ -15,4 +15,3 @@ ipython
 numpydoc
 sphinx-copybutton
 sphinx_rtd_theme
-myst-parser
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 6af95636b..cd74f6f05 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -41,7 +41,6 @@
     'IPython.sphinxext.ipython_console_highlighting',
     'matplotlib.sphinxext.plot_directive',
     'numpydoc',
-    'myst_parser',
     'sphinx_copybutton',
 ]
 
@@ -60,7 +59,8 @@
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:
 #
-source_suffix = ['.rst', '.md']
+# source_suffix = ['.rst', '.md']
+source_suffix = '.rst'
 
 # The master toctree document.
 master_doc = 'index'
diff --git a/docs/source/copyright.md b/docs/source/copyright.md
deleted file mode 100644
index 99badf64a..000000000
--- a/docs/source/copyright.md
+++ /dev/null
@@ -1,18 +0,0 @@
-License
-=======
-
-GNU Public License v3
----------------------
-
-| Copyright &copy; 2015-2021 Hantao Cui.
-
-ANDES is free software; you can redistribute it and/or modify it under
-the terms of the [GNU General Public
-License](http://www.gnu.org/licenses/gpl-3.0.html) as published by the
-Free Software Foundation; either version 3 of the License, or (at your
-option) any later version.
-
-ANDES is distributed in the hope that it will be useful, but WITHOUT ANY
-WARRANTY; without even the implied warranty of MERCHANTABILITY or
-FITNESS FOR A PARTICULAR PURPOSE. See the [GNU General Public
-License](http://www.gnu.org/licenses/gpl-3.0.html) for more details.
diff --git a/docs/source/copyright.rst b/docs/source/copyright.rst
new file mode 100644
index 000000000..0f56c1596
--- /dev/null
+++ b/docs/source/copyright.rst
@@ -0,0 +1,23 @@
+.. role:: raw-html(raw)
+    :format: html
+
+*******
+License
+*******
+
+GNU Public License v3
+*********************
+| Copyright :raw-html:`&copy;` 2015-2020 Hantao Cui.
+
+ANDES is free software; you can redistribute it and/or modify it under
+the terms of the
+`GNU General Public License <http://www.gnu.org/licenses/gpl-3.0.html>`_
+as published by the Free Software Foundation; either version 3 of the
+License, or (at your option) any later version.
+
+ANDES is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+See the
+`GNU General Public License <http://www.gnu.org/licenses/gpl-3.0.html>`_
+for more details.
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 282977334..94487128f 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -22,45 +22,39 @@ ANDES is a Python-based free software package for power system simulation, contr
 It establishes a unique **hybrid symbolic-numeric framework** for modeling differential algebraic
 equations (DAEs) for numerical analysis. Main features of ANDES include
 
-..
-   ANDES offers a symbolic library for discrete components
-   and transfer functions that can be easily imported to DAE models.
-   ANDES supports power flow calculation, time domain simulation and eigenvalue analysis for transmission
-   networks.
-
-- a unique hybrid symbolic-numeric approach to modeling and simulation that enables descriptive DAE modeling and
+* a unique hybrid symbolic-numeric approach to modeling and simulation that enables descriptive DAE modeling and
   automatic numerical code generation
-- a rich library of transfer functions and discontinuous components (including limiters, dead-bands, and
+* a rich library of transfer functions and discontinuous components (including limiters, dead-bands, and
   saturation) available for prototyping models, which can be readily instantiated as multiple devices for
   system analysis
-- industry-grade second-generation renewable models (solar PV, type 3 and type 4 wind),
+* industry-grade second-generation renewable models (solar PV, type 3 and type 4 wind),
   distributed PV and energy storage model
-- comes with the Newton method for power flow calculation, the implicit trapezoidal method for time-domain
+* comes with the Newton method for power flow calculation, the implicit trapezoidal method for time-domain
   simulation, and full eigenvalue calculation
-- strictly verified models with commercial software. ANDES obtains identical time-domain simulation results for
+* strictly verified models with commercial software. ANDES obtains identical time-domain simulation results for
   IEEE 14-bus and NPCC system with GENROU and multiple controller models. See the verification link for details.
-- developed with performance in mind. While written in Python, ANDES comes with a performance package and can
+* developed with performance in mind. While written in Python, ANDES comes with a performance package and can
   finish a 20-second transient simulation of a 2000-bus system in a few seconds on a typical desktop computer
-- out-of-the-box PSS/E raw and dyr file support for available models. Once a model is developed, inputs from a
+* out-of-the-box PSS/E raw and dyr file support for available models. Once a model is developed, inputs from a
   dyr file can be readily supported
-- an always up-to-date equation documentation of implemented models
+* an always up-to-date equation documentation of implemented models
 
 ANDES is currently under active development. To get involved,
 
-- Follow the tutorial at
+* Follow the tutorial at
   `https://andes.readthedocs.io <https://andes.readthedocs.io/en/stable/tutorial.html>`_
-- Checkout the Notebook examples in the
+* Checkout the Notebook examples in the
   `examples folder <https://github.com/cuihantao/andes/tree/master/examples>`_
-- Try ANDES in Jupyter Notebook
+* Try ANDES in Jupyter Notebook
   `with Binder <https://mybinder.org/v2/gh/cuihantao/andes/master>`_
-- Download the PDF manual at
+* Download the PDF manual at
   `download <https://andes.readthedocs.io/_/downloads/en/stable/pdf/>`_
-- Report issues in the
+* Report issues in the
   `GitHub issues page <https://github.com/cuihantao/andes/issues>`_
-- Learn version control with
+* Learn version control with
   `the command-line git <https://git-scm.com/docs/gittutorial>`_ or
   `GitHub Desktop <https://help.github.com/en/desktop/getting-started-with-github-desktop>`_
-- If you are looking to develop models, read the
+* If you are looking to develop models, read the
   `Modeling Cookbook <https://andes.readthedocs.io/en/stable/modeling.html>`_
 
 This work was supported in part by the Engineering Research Center Program of
diff --git a/docs/source/modeling.rst b/docs/source/modeling.rst
index fee114e08..f089a5b76 100644
--- a/docs/source/modeling.rst
+++ b/docs/source/modeling.rst
@@ -990,6 +990,22 @@ Value Selector
 .. autoclass:: andes.core.block.LVGate
     :noindex:
 
+Naming Convention
+-----------------
+
+We loosely follow a naming convention when using modeling blocks.
+An instance of a modeling block is named with a two-letter
+acronym, followed by a number or a meaningful but short variaiable name.
+The acronym and the name are spelled in one word without underscore, as
+the output of the block already contains ``_y``.
+
+For example, two washout filters can be names ``WO1`` and ``WO2``.
+In another case, a first-order lag function for voltage sensing
+can be called ``LGv``, or even ``LG`` if there is only one Lag
+instance in the model.
+
+Naming conventions are not strictly enforced. Expressiveness
+and concision are encouraged.
 
 Examples
 ========
diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst
index 005eda16f..c0c10fb14 100644
--- a/docs/source/release-notes.rst
+++ b/docs/source/release-notes.rst
@@ -6,6 +6,18 @@ Release Notes
 
 The APIs before v3.0.0 are in beta and may change without prior notice.
 
+v1.4 Notes
+----------
+
+v1.4.0 (2021-09-xx)
+```````````````````
+This release highlights the distributed energy resource protection model.
+
+- Added ``DGPRCT1`` model to provide DG models with voltage-
+  and frequency-based protection following IEEE 1547-2018.
+- ``REECA1E`` supports frequency droop on power.
+- Throws TypeError if type mismatches when using ExtAlgeb and ExtState.
+
 v1.3 Notes
 ----------
 v1.3.12 (2021-08-22)
diff --git a/examples/demonstration/1.1 demo_DGPRCT1.ipynb b/examples/demonstration/1.1 demo_DGPRCT1.ipynb
new file mode 100644
index 000000000..a8f47f97a
--- /dev/null
+++ b/examples/demonstration/1.1 demo_DGPRCT1.ipynb
@@ -0,0 +1,599 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# ANDES Demonstration of DGPRCT1 on IEEE 14-Bus System\n",
+    "\n",
+    "Prepared by Jinning Wang. Last revised 1 September 2021.\n",
+    "\n",
+    "## Background\n",
+    "\n",
+    "One load adjustment scenario is used to demonstrate ANDES `DGPRCT1`.\n",
+    "\n",
+    "In the modified IEEE 14-bus system, 10 PVD1 are conencted to `Bus 4`, and 1 DGPRCT1 is added aiming at `PVD1_2`.\n",
+    "\n",
+    "## Conclusion\n",
+    "\n",
+    "`DGPRCT1` can be used to implement protection on `DG` models."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "source": [
+    "import andes\n",
+    "from andes.utils.paths import get_case\n",
+    "\n",
+    "andes.config_logger(stream_level=30)"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "source": [
+    "ss = andes.load(get_case('ieee14/ieee14_dgprct1.xlsx'), setup=False,\n",
+    "                no_output=True)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Working directory: \"/home/jwang175/andes/examples/demonstration\"\n",
+      "Loaded config from file \"/home/jwang175/.andes/andes.rc\"\n",
+      "Using generated Python code.\n",
+      "Parsing input file \"/home/jwang175/andes/andes/cases/ieee14/ieee14_dgprct1.xlsx\"...\n",
+      "Input file parsed in 0.1286 seconds.\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "source": [
+    "ss.setup()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "System internal structure set up in 0.0439 seconds.\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "source": [
+    "# use constant power model for PQ\n",
+    "ss.PQ.config.p2p = 1\n",
+    "ss.PQ.config.q2q = 1\n",
+    "ss.PQ.config.p2z = 0\n",
+    "ss.PQ.config.q2z = 0\n",
+    "\n",
+    "# turn off under-voltage PQ-to-Z conversion\n",
+    "ss.PQ.pq2z = 0\n",
+    "ss.PFlow.run()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "-> System connectivity check results:\n",
+      "  No islanded bus detected.\n",
+      "  A total of 1 island(s) detected.\n",
+      "  Each island has a slack bus correctly defined and enabled.\n",
+      "\n",
+      "-> Power flow calculation\n",
+      "   Sparse solver: KLU\n",
+      " Solution method: NR method\n",
+      " Sparse addition: Fast in-place (kvxopt)\n",
+      "Power flow initialized.\n",
+      "0: |F(x)| = 0.5605182162\n",
+      "1: |F(x)| = 0.006138129294\n",
+      "2: |F(x)| = 5.708068995e-06\n",
+      "3: |F(x)| = 5.716302431e-12\n",
+      "Converged in 4 iterations in 0.0093 seconds.\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "metadata": {
+    "scrolled": true
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "source": [
+    "ss.TDS.config.tf = 20\n",
+    "ss.TDS.run()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "\n",
+      "-> Time Domain Simulation Summary:\n",
+      "Sparse Solver: KLU\n",
+      "Simulation time: 0-20 s.\n",
+      "Fixed step size: h=33.33 ms. Shrink if not converged.\n",
+      "TGOV1.LAG_lim at limits <VMIN>\n",
+      "\n",
+      "  idx    | Flag | Input Value | Limit\n",
+      "---------+------+-------------+------\n",
+      " TGOV1_2 | zl   | 0.300       | 0.300\n",
+      " TGOV1_3 | zl   | 0.300       | 0.300\n",
+      "\n",
+      "\n",
+      "Initialization for dynamics was successful in 0.0433 seconds.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<Toggler 1>: StaticLoad.PQ_12 status changed to 1 at t=1.0 sec.\n",
+      "<Toggler 2>: StaticLoad.PQ_12 status changed to 0 at t=10.0 sec.\n",
+      "100%|████████████████████████████████| 100/100 [00:05<00:00, 18.80%/s]"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Simulation completed in 5.3208 seconds.\n"
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "source": [
+    "ss.exit_code"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Results"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### System frequency and lock flag\n",
+    "\n",
+    "The lock flag is raised at `Tfl1` after the frequency drop below `fl1`"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "source": [
+    "ss.TDS.plt.plot(ss.BusFreq.f,\n",
+    "                a=(3),\n",
+    "                ycalc=lambda x: 60*x,\n",
+    "                hline1=ss.DGPRCT1.fl1.v,\n",
+    "                hline2=ss.DGPRCT1.fl2.v,\n",
+    "                title='PVD1\\_2 Connected Bus Freq (measured by BusFreq 4)')\n",
+    "\n",
+    "ss.TDS.plt.plot(ss.DGPRCT1.ue,\n",
+    "                title='DGPRCT1\\_1 lock flag (applied on PVD1\\_2)')"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(<Figure size 600x400 with 1 Axes>,\n",
+       " <AxesSubplot:title={'center':'DGPRCT1\\\\_1 lock flag (applied on PVD1\\\\_2)'}, xlabel='Time [s]'>)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 31
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### PVD1_2 read frequency and frequency signal source\n",
+    "\n",
+    "The `PVD1_2` read frequency is locked, but the signal source (in the `BusFreq 4`) remains unchanged"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "source": [
+    "ss.TDS.plt.plot(ss.PVD1.fHz,\n",
+    "                a=(0,1),\n",
+    "                hline1=59.9,\n",
+    "                hline2=59.8,\n",
+    "                title='PVD1 Read f')\n",
+    "\n",
+    "ss.TDS.plt.plot(ss.DGPRCT1.fHz,\n",
+    "                hline1=59.9,\n",
+    "                hline2=59.8,\n",
+    "                title='BusFreq 4 Output f')"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(<Figure size 600x400 with 1 Axes>,\n",
+       " <AxesSubplot:title={'center':'BusFreq 4 Output f'}, xlabel='Time [s]'>)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### PVD1_2 power command\n",
+    "\n",
+    "`PVD1_2` power commands are locked to 0 **immediately**.\n",
+    "\n",
+    "Once the protection was released, they returned to normal **immediately**."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "source": [
+    "ss.TDS.plt.plot(ss.PVD1.Psum,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 P\\_tot (active power command)')\n",
+    "\n",
+    "ss.TDS.plt.plot(ss.PVD1.Qsum,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 Q\\_tot (reactive power command)')"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(<Figure size 600x400 with 1 Axes>,\n",
+       " <AxesSubplot:title={'center':'PVD1 Q\\\\_tot (reactive power command)'}, xlabel='Time [s]'>)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 33
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### PVD1_2 current command\n",
+    "\n",
+    "Consequently, `PVD1_2` current commands are locked to 0 **immediately**.\n",
+    "\n",
+    "Once the protection was released, they returned to normal **immediately**."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "source": [
+    "ss.TDS.plt.plot(ss.PVD1.Ipul,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 Ip\\_ul (current command before hard limit)')\n",
+    "\n",
+    "ss.TDS.plt.plot(ss.PVD1.Iqul,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 Iq\\_ul (current command before hard limit)')"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(<Figure size 600x400 with 1 Axes>,\n",
+       " <AxesSubplot:title={'center':'PVD1 Iq\\\\_ul (current command before hard limit)'}, xlabel='Time [s]'>)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### PVD1_2 output current\n",
+    "\n",
+    "As a result, `PVD1_2` output current decreased to 0 **gradually**.\n",
+    "\n",
+    "When the protection was released, they returned to normal **gradually**.\n",
+    "\n",
+    "Here, the `PVD1` output current `Lag` time constant (`tip` and `tiq`) are modified to 0.5, which is only for observation.\n",
+    "\n",
+    "Usually, power electronic device can response in ms level."
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "source": [
+    "ss.TDS.plt.plot(ss.PVD1.Ipout_y,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 Ip\\_out (actual output current)')\n",
+    "\n",
+    "ss.TDS.plt.plot(ss.PVD1.Iqout_y,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 Iq\\_out (actual output current)')"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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UO8Jr/vxh/vW9d3D3mkn4ixvhlzvgmh1eh5ZS4+PjHD9+nC1btlBcXOx1ODmnp6eHtrY2WltbCQQCNDc3A9Dd3U1fXx+7d++msbERgI6ODtra2ujs7KShoYGWlhbq6+tn7Wvnzp0Eg0Gam5tpbGyMu+/eXuePubq6OpqamvBKIj97K9HnJtUtN31zbK+Zp2yhOqkuv4y1dgKYiDxPl2a3bHHqhZ9w+ui/U/TSw1w19hR/MNnMd8K3876KC2zJD3N6zT2cqb2asvXXcs/GG/jphgBlRfnA+y7b16tm/rd5nncsW/ZjkPS0wV9Cns9wom8UtrijN4bPexuUZL1AIEBLSwtdXV0EAgH27Nkzq7yuro6enh727NlDY2MjDQ0NVFdX09zcPCuxiexr3759+P1+GhoaAObdd3NzMzt37uTQoUOztgeDQQ4ePMihQ4c4fPhwwseSbD2vpTq56QGnk7C1Nhi13R8pS6JOqstlBZzsG+WLXae57ZEP85rQI6yxeTxfdDPPbNrF+2+5j/9zYz215fd7HaZkuII8Hxv8xZzoHXX62bzlb2DTnV6HJTmuubmZvXv3ziQmfr9/pkUm0qITLZIIJaKtrY0dO3bQ3t4+04LT1dXF0aNHCQaD9PXN9ff95ZKtlw5SmtxYa4PGmB7i9HWx1sbrTJxQnVSXS2qEQyEef+hzFB79e/YM/SIvFlzNb255O1Ubf5Wrf+YN3FSZ0Eh8kUW5qqbMabkBuP1XvA1GBCeZAadVJPL/5uZmduzYMWtb7OsTtXPnTvbu3TuT3NTX11NfX09HR8ei9pNsvXSwEp0MWoDmyBN3Ar29Uc8DMbMFL1hnBcplGdlwmGNf/xQn/+RWbv/Br+OzYX57Rx1Hfr+Bpl97gNt2/BLlSmwkRTbVlL6c3LzwEDzzDW8DkiXp7Oyko6OD9vaXu0cGg0G2bt3qYVSLc+zYMerr62clLQ0NDfj9/lnHBdDe3s6uXbsWtf9du3YRDAbp6srdv9dTvvyCtbbdGLMnMiswUBszE3ADTmLRnmidVJfL8nny1ACPf/5BfmX40zxe8jOM3fsJbrhjBzd4HZjkjNqyQoKj7pwbj30Ghi/A9W/0NiiPjE2G6L447HUYM+pWl1NSmPhaRD09PdTU1BAIBNixY8dMy0RnZyc1NXMNdk0fwWCQ9vZ2jh49ykMPPXRZeVNTE21tbbNuQcVryVlI5PVHjx69rA9PrliRtaXmW7fJHZ102QilhdZ6SnW5LM3U5ASf+8ZD/OGPYduq13Hbfffyirvf4nVYkoPKi/MZGneTm7I1cO4n3gbkoe6Lw7zpr7/vdRgzvvbhV3PzFVUJv76np4eGhgZaW1tnOtcCHD58mB070nME3NGjR2e1xjQ0NMzZf2b37t20trbS1dVFfX09PT09BALzTc8mc9Gq4LLszp18gcF/fgf3T51j8DVf5/0NN5Ofp2HW4o2K4nyGJ6ax1mLKVsHIxYUrZam61eV87cOv9jqMGXWryxf1+khCc+DAAVpaWma2Hz16lJ07d8487+rq4oEHHmDbtm3s3bt32ROExbSmbNu2LeGh2fX19QQCAdra2maGhyczrDuyWGouJ0ZKbmRZPf/o96j+yjupoIBLb/5nPrTtVq9DkhxXUVxA2MLoZIiy4iqYGPI6JM+UFOYtqqUkHUX6kkS33MQ+r6+vJxgMpiSxAbjvvvs4duzYsu8XnI7F+/fvp62tLekV3Y8ePQo4iVWu0p/Tsmwe//YhNn757fTmr6Pg/d/hum2v8zokESqKnL/hhsanoSYAda+D0LTHUUmyYm/VdHZ2XtavJDJ0ORWJTWR+mVRpamqaScyiE7bFaGtro6WlZdF9dbKJkhtZFv/1/EV+76F+jlQ2cNVvPcSqdZu8DkkEcG5LAU6/m6vvg18+CHlqtM5Ufr9/1kW7ra3tsiQgMttvtJ6eHlpbW2lvb6e5uZmurq6ZbR0dHTMz/fb09My8BphJNMBJbPbv309NTQ2dnZ0pO76GhgY6OjqS6gzc2tpKMBiM268n2blqMm2OG9BtKVkGTx75Hh/+ah+3X30bd77zfRTmK2eW9FFRXADA4Pg0hEMwPgBFlUpwMlQgEGDbtm0znXS7urrYvXv3rNccOXJkVgfjYDDIzp07Z24l1dXV0dbWxtatW2e2RZKaQCBAQ0PDzAy/0UlMJNlY6HZXT08PHR0dHD16dCaBampqSrglpbm5mZ6e+HPKRpZ26OzsJBAI0NrqjI3p7e0lGAxSV1d32UzCkXgOHDhAV1cXe/fuZfv27XEnDFyOeukgpWtLZStjTCUwoLWl4OTzj1P1mdfz3fL72fGbbRQXJD6sU2QlnA6O8ao/+zb//J47eE3JcfiHHfDBH8Ka7J2QIJfWlqqurqa/v3/Wtq1bt/LJT35yJhlpb2+nu7ublpYWgsEg9913H83NzTPb4OVJ9BobG2ltbcXv99PU1DSznEGkJaiuro7u7u6VPcgMki5rS+lPbEnaYLAX+7l30O+r4TXva1FiI2lp1m2pQnddsYn0metFFqe6unqmo+1cE9xFhlJHq6urA+DgwYOzEpWIo0ePzrRIHDlyZOY1nZ2dbNu2jc7OTnp6emb2m6rbUrI8lNxIUmw4zPP/8D6qQ3343vFZKv21XockEld5YT7GuB2KC92hx5NKbjJRMBicWUQyGAzODJmO6OnpmbllFD07b2TG3sgK3Nu3b2fXrl10d3fT0dFBa2vrrEn1du/ePTMLcn19PQcPHmTbtm0EAgFqampob2/P6ZFImUC3pZKg21LwcOfXeO33f5mj2/8P2+5/wOtwROZ1y4P/yW/cdw0PbK2CPw/A7n+DG97sdVgpk823pdrb26mpqaGnp2dR/Vgitm7dykMPPZTTI4lSKV1uS6lHnSza+cFxPvxfBbxny9/z0fvf4XU4IguamaW4KNJyM+JtQJK0ZCa1i5bMcgaSeXRbShbtswc/R1G+4T270r/HvAg4/W4Gx6chrxD2HIeb9bObiyIji+YaiSTZQ8mNLMrj3znER099hL/6mQGqSgu8DkckIeVFzhIMGAOlNRoGnqP27NlDd3d3Ti9LkCuU3EjCpqcmqfqvP+SnhbdyV8PbvQ5HJGFlRfmMTLizEn/516HrX70NSERSSn++SMK6vvo33BE+yfNv+FuMT3mxZI6ZlhuA00ehODcHAojkCl2hJCHjo8NsfvKvOFpxH9fcfo/X4YgsyqyWm8KynF48UyQXqOVGEvKFrtN0T9/Pr73l/V6HIrJo5UX5jEyEnCeFZRotJZLl1HIjC5oKhfnb75/m0k3vZdPVN3sdjsiizbotVVihSfxEspxabmRBXV//JE3Dh7njNZ/yOhSRpJRFJzfbfs1ZQFMkRSKLW7a2thIIBGZWGO/u7qavr4/du3fPLPUQmTU5spJ5S0vLrKUjenp62LlzJ8FgkObmZhobG+Puu7e3F3CWlFjqXEDZQMmNzMuGw9Q+3sZtFbXcsMHvdTgiSSkvymNkYhprLeaaHQtXEFmCQCBAS0sLXV1dBAIB9uzZM6u8rq6Onp4e9uzZQ2NjIw0NDVRXV9Pc3HzZmliBQGBmyYnIelfz7Tuy0GdkVfOIYDDIwYMHOXTo0GWrhs8nMjdQZLHQ6OUu0pluS8m8nj32ba4OdcMd+ktAMldZUT7TYcvEdBjOPQk//bLXIUkOa25uZu/evTPP/X7/TItMPD09PTOJzULa2toIBoO0t7fPbOvq6uLgwYMEg0H6+voSjnPv3r3s2bOHPXv2zMS2Y0dm/HGg5EbmNfxff8cps45b7nmb16GIJK2syGmkHpmYhme+Af+xd4EaIqkTWf4hsro5OAlPZ2fnrG2xr0/Uzp07ZyVP9fX1NDU1LWrywmAwSFdXV9wYM2GGZyU3Mqe+Sxe4deBhTl39S/jy8rwORyRp5TPJTUijpTJcZ2fnzIrdEcFgkK1bt3oY1eIcO3aM+vr6WUlLQ0MDfr9/1nGBs1Dorl27FrX/yCro0SujJ+Po0aOzEplIchQvAUs3Sm5kTl99doT7p1u57vUf9DoUkSWJtNwMT0w7i2dODoO1Hkcli9XT00NNTQ319fW0tLTMbO/s7KSmpsbDyBITDAZpbW3l6NGjPPTQQ5eVNzU1XXZrKpmFPiOvP3r0aLKh4vf76e/vn9UHqLOzEyAjlq9Qh2KZ0xe7TrH5uldQXbva61BElqS8yGl5HJmchsJywMLUqNOKk2uGzjmPaCV+qN4MU+Nw8ZnL62y4zfn30vOXt3r5r3TW6xq5BAOnZpcVVUBtnTM67dyTs8sq1jmPRYj0PWltbZ3VB+Xw4cNp2xfk6NGjs1pjGhoaLusEHLF7925aW1vp6uqivr6enp6etEok9u/fT1tbW0asqq7kRuJ66ZkuPnHxfZze9o9ehyKyZLNabirWw5V3QWgSyMHk5uin4bt/NnvbLbvg7Z+EwdPQ/prL63xswPn3yx+AU0dml721HV6xG376JfjGb88uq3sdvPNLTkIUu9/X/C7cu29RoUcSmgMHDsxquTl69Cg7d+6ced7V1cUDDzzAtm3b2Lt377InCItpTdm2bVvCQ7Pr6+sJBAK0tbXNDA9PZlh35LbRch733r172b17d8YMM1dyI3Gd+d4/cZNviCtuv93rUESWbFaH4uteBe/5pscReWjbr8F1b5i9rcTv/Ft5BTR9d+66v/B38VtuAG56K2zcPrusqML5t7Ds8v0ustUmItKXJLrlJvZ5fX09wWAwJYkNwH333cexY8eWfb/gdNqNtJAk27clcjtq27ZtyxJTR0dHxs2fo+RGLmPDYTad/RbPVN/LHcWlXocjsmRlhVHJDUBoGowBXw52lJ/vdlBB8cu3oOJZdc3cZWWrnEc8vrz597sIsbdqOjs7L5sbJjLkORWJTWR+mVRpampi7969My0lyWhra6OlpWVZbh9F+tlEEptUfrbLSR2K5TIvPn2EjfYsxbe+1etQRJZFns9QUpDH8ETI6TfyR7Vw8kdehyVJ8Pv9sy7abW1tl80BE5ntN1pPTw+tra20t7fT3NxMV1fXzLaOjo6ZmX57enpmXgPMtACBk9js37+fmpqamYt+Ko6voaGBjo6Oy5K2RLS2thIMBuP261nMHDfgHG90/5/IZ5MJnbdT3nJjjNkDBN2nfmtt61LrLKXcGNMANAOHgR5gB3DEWtuR8EFludM//gqrbAnXv/JNXocismzKi92VwSOdiCe0vlQmCgQCbNu2baaTbldX12UtHEeOHJnVwTgYDLJz586ZW0l1dXW0tbWxdevWmW2RC3cgEKChoWFmht/oJCaSbCx0u6unp4eOjo6ZodStra00NTUl3JLS3Nw851wykaUdOjs7CQQCMzMI9/b2EgwGqauru2wG4kg8Bw4coKuri71797J9+/aZJSDiCQaD3HfffbOSu4i5OkSnFWttyh7AHmBP1PMGoG0pdZahvBHoByzQDTQlcVyVgB0YGLDZ6A0f/7b9o3/+qtdhiCyr1/75d+yffP0pa8eC1j5Yae2TX/A6pJQZGxuzTz31lB0bG/M6lJTz+/2Xbauvr7fHjh2bed7W1mb37NljrbW2v7/f1tfXz9pmrbVNTU320KFD1lprW1pabFtb28z2w4cPz7wuEAik5DiyRSI/ewMDA9a9BlfaFOUfqb4ttQ+YGQNnre0EFuqRtFCdpZYDbLHWGmttnbW2HZnxUu8IT50fZVv98nREE0kXZUV5zmipwnJngybyy0jV1dUzHW3nmuAucislWl1dHQAHDx6cuWUV2QZOJ9xIS8aRI0dmXtPZ2cm2bdtmZuaN7DdVt6VkeaQsuTHGBHBuCQXjlMVdJGOhOkstX+wxRNUtMsZURh5ARbL7SndnOv+GzxX+CXdfXet1KCLLqqzQvS3ly4P8EmciP8kowWBwZhHJYDA4M2Q6oqenZ+aWUfTsvJEZeyMrcG/fvp1du3bR3d1NR0cHra2tsybV271798wsyPX19Rw8eJBt27YRCASoqamhvb192UYiSWoYm6JZOt1k4rC11sRs7wcesHH6uCxUB6cfTdLl1toOY0wjUAP0uf/WWWvnXWjGGPMx4MHY7QMDA1RWVs5XNeM83vKz5IUnuHnfPMNBRTLQe//pCMbAp961HfpfgtJaZ7biLDQ+Ps7x48fZsmULxcXFXoezrCIdWnt6ehbVjyVi69atPPTQQxkxEV0mSuRnb3BwkKqqKoAqa+1gKuLwYih4JKlIpk5wieUAXQDW2h4AY0yTMeaQtXbnHHUB9gMfj3peAZya47UZa3xslGtGH+OJq9/vdSgiy66sKJ8LQ+POk+qrvA1GkrbUuVaSWc5AMk/CyY3b4pHIoPv91tr5VutKZgzZQnUSLo8kNVEOAm3GmLi3s9w6E8BE5LkxJt7LMt7zRw9zi5lgbf39XocisuzKivIZuRRynnz7T6B8DdzxgLdByYqKjCxKt2UNZPklnNy4t5EWM1x6rjXR/fOULVRnqeUYYxqjb4lZa4NushLAbdXJVcM//SYXqWbzDdsXfrFIhikvynt5Er8Tjyi5yUF79uzJjGHMsmQp61DstpAE3U6+sWVxu5kvVGep5cYYP3AoutzdBnMnRjnjz8beyr/UfRzj09yOkn3KivKd0VIAxVUwMeRtQCKSMqm+iu3HmWcGmLm11R71POBOuJdwnaWUu7edWmNuTTUBHXPdksoVwdFJnrgwxVU33uF1KCIpUV6U/3LLTVGFkhuRLJbS5MY6MwP7jTGNbpKx3VrbHPWSyGzBCddZajmw3xizJ/IAahfoTJwTXvjhv/P3+R/nzo3ZNbJCJKKsKJ+RyRDhsM2Z5CZVo2FF5pIuP3MpHy1lZy+N0BFT1s7sVpcF6yy1PNJ6M2/QOWj62cO8Iu9F1q3R/DaSnSIrg49OhSi//n5Ye7PHEaVOQUEBAKOjo5SUlHgcjeSS0dFR4OWfQa9oVXABoPbSEU5V3s66LB0JJlJR9PLK4OWB13obTIrl5eXh9/u5cOECAKWlpVk7ylPSg7WW0dFRLly4gN/vJy8vz9N4lNwIg8FeAtPdHLvqV7wORSRlIi03Q+PTrLWn4PQxuOHnIUsv+uvWrQOYSXBEVoLf75/52fOSkhvh+KPf5hXGsv7W13kdikjKlLvJzfDENJx7BL74Pvi9My+vEp5ljDGsX7+eNWvWMDU15XU4kgMKCgo8b7GJUHIj/GB0E583H+FP627xOhSRlKkojrTcTEGxu2zKxFDWJjcReXl5aXPBEVkpmtBEeOS8j3NX3q/5bSSrVRY7HRyHxqed0VKQEyOmRHKRrmY5zobD7DjxCRqqL3odikhKlUe33MwkNylZs09EPKbkJsed6n6Sd/J1bvGPex2KSErl+QxlhXlOy01JNay50euQRCRF1Ocmx5176r/ZBFx16z1ehyKScuXF+U5yUxWADz7idTgikiJquclx4ZNHOGk2UFWz2utQRFKuorjASW5EJKspuclx1cGfcK4ye2dqFYlWUZzv9LkBaK2DH7V5G5CIpISSmxw2FQpzaOJO+ra8xetQRFbErJYb44NxdSgWyUZKbnJYz8URPjn1evy3vsHrUERWREVxPkMTbstNUYVGS4lkKSU3Oezks0e52/cEN2yo9DoUkRVRUZTPcKTlJkdWBhfJRUpuclj5UwdoKfr0zORmItmuIjJaCtRyI5LFNBQ8h5UHn+Z86TVs8DoQkRVSUVzAYCS5+fm/gvxibwMSkZRQy02OsuEwGydeYHyVRkpJ7pg1WqomAJVK7UWykZKbHHXhTA9+hinZdJvXoYismIriAiamw0xOh+GnX4KH/rfXIYlICii5yVHHT57lSPha1l233etQRFZMeVHU+lLnnoQnD3kckYikgpKbHHVsfD3v9f0xazfWeR2KyIqpdBfPHJ6Y1mgpkSym5CZHnT39EtevKcUY43UoIiumwh0ZODTuJjfjg2Ctx1GJyHLTaKkc9e7jv0Nv1U3A3V6HIrJiKtyWm8HxKSj2gw3B5AgUlXsbmIgsK7Xc5KDQ9DQbp09gV13rdSgiKyqS3AyNT8Pam+Ce3/E4IhFJBSU3OejciecoNlOUXXGT16GIrKjy6ORmzQ3wut9Xq41IFlJyk4Mu9jwBwJq6V3gcicjKKsrPozDfx/D4FEyNQ8/DMNLrdVgissyU3OSgoXPdDNkS1mzY4nUoIiuuMrIEw/gA/Mtb4NSPvQ5JRJaZkpsc9KXC+3nf6s9gfDr9knsqigsYmpiG4ipnw/iAtwGJyLLT1S0HvXBhmCvXrvI6DBFPzCzBUFAMeUVKbkSykJKbHGPDYVovfoAd5kdehyLiiYri/JcXzyzxK7kRyUIpn+fGGLMHCLpP/dba1qXWSaDcD+wCdlprdyxHTNni0rkTXG9OMF6lESKSmyqLCxgccxfPXHUt5BV6G5CILLuUJjduEoG1tt193mCMabPWNidbJ4HyemAb4AdqliOmbHLhxadYDVRvut7rUEQ8UVVSwJngmPPk3V/zNhgRSYlU35baB7RHnlhrO4GmJdaZt9xa2+UmLj3LGFPWGDn7LCFrWHuVkhvJTVUlBQxEWm5EJCulLLkxxgRwbvkE45Q1JFMnmX0uNaZsE7r0Aud9qykuKfU6FBFPVEYnN1//bfjMLm8DEpFll8rbUoE5tgdxbhklUyeZfS41JowxRUBR1KaKBN4rLX294Gcpqb2V/+l1ICIeqSopYHB8GmstxoZh6KzXIYnIMvNitFQfcfrCLLFOMvtcTP19wEDU49QS3stTRwerGdv4Sq/DEPFMZUkBobBlODLXjUZLiWSdhFtujDGNwO4EXrrfWts1T3kySchCdZaS2CRSfz/w8ajnFWRgghMOhdjV9/f4r30ncIvX4Yh4oqqkAICBsSkqiqtgPOhtQCKy7BJObqy1HUDHIvY9V4de/zxlC9VJZp9LjQlr7QQwEXlujEngrdLPxbMv8h7f13ms8Oe9DkXEM5HkZnAs0nIzCOEwaMZukayRst9ma20PEHQ78caWdSZTJ5l9LjWmbHLxpacADQOX3BbdcsP198MD3/Y4IhFZbqn+U2U/MDMKyb211R71PBCZdybROgmUR8x1qynR+lln5MxzhKxh/WYlN5K7ZiU35Wvginq12ohkmZT+Rrsz//qNMY1uErE9ZrK8BqB5MXUWKo9KmJqBemNMi/u6RGPKWuG+Hs77VlNYVOx1KCKeqSx27sYPjk3B4Bn4xu9A8ITHUYnIcjLWWq9jyDjGmEpgYGBggMrKSq/DSdin2j5O2fBJ3vE//sLrUEQ8dfOD/8lv3ncND9xk4a/r4V1fgy13ex2WSE4YHBykqqoKoMpaO5iK91BbbA7596k76Lry3V6HIeK5mVmKi6ucDRoOLpJVlNzkkDV9R7mmYtLrMEQ8V6nkRiSrKbnJEaPDA3wy/CC3TxzxOhQRz1WV5DM4PgV5BVBQpuRGJMsouckRF0++AEDZ2rlWoBDJHbMWz7zz/bD2Jm8DEpFllcq1pSSNBM92cxVQu/Ear0MR8VxlcQEvXBx2ntz3B94GIyLLTi03OWL84nEmbR6r1l3ldSginpvVcnPpBTj/lLcBiciyUnKTIwZHxujOC5CXr8Y6kaqSAmeeG4DOB+Hw//I2IBFZVrrS5YgvFb2ZgQ2v5zNeByKSBqpKnZYbay2mpBrO/9TrkERkGanlJkec6htlU3Wp12GIpIWqkgKmQpaxqRCU1sBYv9chicgyUnKTI9p6383rx77mdRgiaaEyen2pkmoY6/M4IhFZTkpucsDIUJD1XKK8strrUETSQmTxzMGxaShfB4UVEA57HJWILBf1uckBl868SBlQumaL16GIpAW/m9wERyfhtnc4DxHJGmq5yQGD518EwL92s6dxiKSL6tJCAPpHpzyORERSQclNDhjrPQFA7forPY5EJD1UlhRgDPSPTkJvN3z8Rjh1zOuwRGSZKLnJAV2ld/MreS0UFWu0lAhAns9QVVLgJDcFpTB4GkYveR2WiCwTJTc54KWRfIJ+rZ0jEq2mtJDgqDtaCmBUI6ZEsoU6FOeArSf+kWvza4C7vQ5FJG34SwvoG5mEgmKn9UZz3YhkDbXc5ICtg9/mRnq8DkMkrVSXFjqjpQBKajTXjUgWUctNDqgJX+RcxQavwxBJK9VlhRy/NOI82flpqFjvbUAismyU3GS50eEBqhihoHqj16GIpJXq0gK6RtyWm013eBuMiCwr3ZbKcr1njgNQUqth4CLR/KWFzmgpgJ98EX7U7m1AIrJslNxkuXPjBfzl9Nuo3Hij16GIpJWaskIGxqYIhS0c/x489m9ehyQiy0TJTZY7MVXFX0w3snqDWm5EolWXFhC2MDizeKZGS4lkCyU3WW7y9BPcW/ICxQV5Xociklb8M0swTEJpDYwquRHJFkpuslzgpQP8vu/TXochknZqyqLWlyqpgckhCGmtKZFsoOQmyxWNXWC4YJXXYYikHX+pszJ4/8gkrL0R7mhSciOSJTQUPMuVTl4iWH6112GIpB1/SdRtqRtvhw23exyRiCwXtdxkucrpPkJla70OQyTtFOb7qCjKd9aXCk3DmUe1vpRIllByk8WstZwNVxPyb/E6FJG05C8roG90EqZGoP210PMdr0MSkWWQ8ttSxpg9QNB96rfWti61TgLlfmAXsNNauyOmrAFoBg4DPcAO4Ii1tiPhg8oQwdEp3jr5h/zd9fVehyKSlmbWlyqqBF8BjPR6HZKILIOUtty4SQjW2nZrbTvQZYxpW0qdBMrrcRIbP1AT5y38QAPQ5j66szGxAbgwOAZY1lQWeR2KSFqqLi2kf2QKjIGyVTB6yeuQRGQZpLrlZh8wc0/EWttpjDmM03KSbJ15y621XTgJT+M877HFWhtc5LFknMnnv80zRQ/Qax8hfp4nktuqSws4MzDuPClbBSNKbkSyQcpabowxAZxbRsE4ZQ3J1Elmn8vBGFNkjKmMPICKVL3XcproP0OxmaJ2jVY7FomntryIvsjimVWbwIa9DUhElkUqW24Cc2wP4twaSqZOMvuMZ5cxpg+nOaPOWrt3gdfvAx5cxP7TwvTAOQYppbK03OtQRNJSbXkhvcMTzpN3fM7bYERk2XgxWiqSVCxnncXsswvotNZ2uH12uo0xhxaosx+oinpsTPC9vDV8nn6fbkeJzGVVWRH9o1NMh9RiI5JNEm65cfuw7E7gpfvdfi9zSeZqu1CdhPdpre2J2XQQaDPGxL3d5daZACYiz40xib6dpwrGLjKUX+t1GCJpq7bcmcivb3SSNc8dgB+1wQd/4HFUIrJUCSc37oiixYwqik0iIvzzlC1UJ5l9zmKMaYweHWWtDbrJSgCnVSdr/F1JExtXW272OhCRNFVb7owkvDQ0yRosXHgKwiHwaaFZkUyWsttSbgtJ0O0EHFvWmUydZPYZzZ3/5lB0fXcbJJgcZZKe0RLyazZ7HYZI2qp1F8/sHZmA0lWAhTGtDi6S6VLd52Y/zpwywMytrfao54HIvDWJ1kmgPOKyW1XubafWmFtTTUBHNg4N/8jQ/+W2qce8DkMkbUVuS/UOTzpDwQFGLnoYkYgsh5QmN+7MwX5jTKObhGy31kbPcROZLTjhOguVRyVMzUC9MaYlZs6b/caYPZEHUGut3bncx+61sZEhft58j3V5A16HIpK2SgvzKS3M49JwpOUGzXUjkgWMtdbrGDKOO9fNwMDAAJWVlV6HE9fpnqe54l/u5MnX/Qu33PMWr8MRSVt3t36bN926gb33bYYTj8CG26Ck2uuwRLLW4OAgVVVVAFXW2sFUvEfK15YSbwz1ngagfNUGjyMRSW+1ZUXOXDcFxVB3r9fhiMgy0KrgWWqs/xwAlbWanVhkPqvKC50+NwA/+Gt47j+9DUhElkzJTZY6m7+Rv5h+O/7adV6HIpLWasuKuBRZguHxz8Pz3/I2IBFZMiU3Weq42ci/Fr2DvHzdeRSZz6qKqCUYylZptJRIFlByk6UKzz/O3UUveB2GSNpz+ty4LTfla2H4grcBiciSKbnJUref+SxN05/xOgyRtFdbXsjYVIiRiWkoXwPD570OSUSWSPcsslTRRC8ThVo0U2Qhq9wlGHqHJym76tVgtPSCSKZTcpOlSqeDXKrY4nUYImkvMkvxpZEJrrzu9XDd6z2OSESWSrelslRlKEg4MuOqiMyptiyyeOYETI3B6S6YGPY4KhFZCiU3Wchay8nwKkJVV3kdikjaqykrxGfg4vAE9L8En7wXzv/E67BEZAmU3GShwbFp3jb5hwxe+3avQxFJe3k+w6ryIi4MTjgdikGdikUynJKbLHRpeAyw1JYVeh2KSEZYW1nMhaEJZ00pXwEMKbkRyWRKbrLQ5PFHeK7oV1k3fcrrUEQywpqKIi4MjoMx7lw3Sm5EMpmSmyw0HjxHoQlRVbPW61BEMsKayiKn5QagZguEp7wNSESWREPBs9DU4AWmrY/K6tVehyKSEVZXFHNhyJ2Z+N1f8zYYEVkytdxkodDwJYKmEl+eJiMTScTayiIuDU8SCluvQxGRZaDkJgv5Ri4y5KvyOgyRjLGmophQ2NI3MglHPgV//2qvQxKRJdBtqSz0xap3MpH3Jj7hdSAiGWJNhTOR3/nBcVZj4PxTEA6BT62fIplILTdZ6OR4CdNVm70OQyRjrKl0kpuLQxPOaCkbgtE+j6MSkWQpuclCb7/099wz8V2vwxDJGKvKizAGLgyNQ8U6Z+PwOW+DEpGkKbnJQq+e+B5Xhk56HYZIxijI81FbVujMUhxJboaU3IhkKiU3WajSDkFpjddhiGSU1RXFnB8ah/J18KtfgY3bvA5JRJKkDsVZZnx0mBIzSX65khuRxXBmKZ6AvHwIvNbrcERkCdRyk2UG+p2JyArKV3kciUhmWVMRNUvxsX+CRz/jaTwikjwlN1mmfyKP/zvVSP7a670ORSSjrK0sdtaXAnj+MPzkC94GJCJJU3KTZfpsGX8dehtla+u8DkUko6z3F3N+aMKZpbjyChg663VIIpIkJTdZZvzSS7zW9xjVxZp8TGQx1lc5sxRfGBqHyvUweNrrkEQkSUpuskzZie/wDwV/TkVJgdehiGSU9VUlAJwdGHdabsYHYHLE46hEJBkpHy1ljNkDBN2nfmtt61LrJFgOUAdgrW1eakyZwo70MWgqqNaimSKLsiGS3ATHYd0tcOcHITTlcVQikoyUttxEkgxrbbu1th3oMsa0LaVOAuUt1tpW99Hsbju8lJgyylgfQ74Kr6MQyTiVJfmUFuZxdmAM1twAr98PJX6vwxKRJKT6ttQ+oD3yxFrbCTQtsc6c5cYYP1Dv/hvRBjQYYwJLiClj5I33M5qnFcFFFssYw/qqYs4E3RFTp49BUDN9i2SilCU3bjLht9YG45Q1JFMnwX1uAwJRRT3uv/5kYnLLiowxlZEHkLZNI/22lPNFm70OQyQjbfCXOC03AP/2dnjigLcBiUhSUtnnJjDH9iDgT7LOvOVu0lIdUxZJWnpwEp/FxgROa8+D85Snjb8vfh9bVpXzGq8DEclA66uKefb8sPNEw8FFMpYXo6X6gMWuDbBQnfnK9wHN8VprFrH//UBV1GPjPK/1VHBkkupSjZQSSca6qhLOBt2Wm8oNMHjG24BEJCkJt9wYYxqB3Qm8dL+1tmue8mQWPVqoTtxyY0wLcMDtOJz0/q21E8BE1H4X2J13Doz8Gs/0vwf4A69DEck4G6qKuTg8weR0mMLKK+D0Ua9DEpEkJJzcWGs7gI5F7Ltnju3+ecoWqpPwPt1krDsmsUkmpowRDoWosUEKi0u9DkUkI633l2AtnB8cZ1Pt1XDuCa9DEpEkpOy2lLW2BwhGjVKKLutMpk6i+4x0Do4kNsYYvzEmkExMmWRooI88Yykor/U6FJGMtKGqGHAn8nvlh+CBb3sckYgkI9V9bvbzcofeSGtKe9TzQNSEewnVSWCf9UA9zvw1ATeRacLpV5PI/jPWUN95AAorlNyIJGO9PzJL8djLG631KBoRSVZKkxt35l+/MabRTSK2x8wW3AA0L6bOfOXu/DYPAS1Ad9SjJdKhOIGYMtZI8AIAJVVrPI5EJDOVF+VTWZzP6eAYjPZBax08902vwxKRRUr58gsxSxt0xJS1E6fVZL4685XPMRR8UTFlsnMlV/MbE3/GP224zutQRDLWpppSTvaNQbEfJgYheMLrkERkkbRwZhYJTuXxrL2Sqoq0nWNQJO1tqi7lVP8o+HxQtUnJjUgGUnKTRcpOfpc/Lvg0JQVaNFMkWZtqSjjZN+o88Su5EclESm6ySFnvE7wx70dpPQ+PSLrbVFPK6eAYobAF/5VKbkQyUMr73MjKMeMDjJjypGZJFBHHxuoSpkKW84PjbLj7t8GGvQ5JRBZJyU0W8U0MMJZX7nUYIhltU7UzCebJvlE2BK7yOBoRSYZuS2WRgslBxvMrvQ5DJKNtjCQ3/WMwcAq+/EHof9HboERkUZTcZJEfFN7Fo/4dXochktFKCvNYVV7kdio28Nhn4OKzXoclIoug21JZ5D9893DrWr/XYYhkvE01JZzsH4WKqyGvCPqOex2SiCyCWm6yyJbhR9nku+R1GCIZb1N1Kaf6xpy5bmq2QF/Gr6srklOU3GSRP574M24b0EJ/Iks103IDUFMHfd3eBiQii6LbUlkiHApRYUfxlfi9DkUk411VU8a5wXHGp0IU3/ZLMDnsdUgisghKbrLE0FCQKmMpKFtwaS0RWcDmVWVYCyf6Rrn2hjd5HY6ILJJuS2WJkaDT16agXMmNyFJtWVUGwPFLIzAxBD/9Moz0ehuUiCRMyU2WGB4Z4UR4NUWVq70ORSTjrSovpKIo30luxvrh0LvgzKNehyUiCVJykyUuFV/JPZN/SeHG270ORSTjGWPYvKqM4xdHoPIKyCvUiCmRDKLkJksMjk0BUFVS4HEkItlhy6oyjveOgC8PqjcruRHJIEpuskT181/gSNH7qSjUiuAiy2HLqjLnthRATUDJjUgGUXKTJezIJUqYIr9ALTciy2HLqjIuDk0wND4FG7dBxTqvQxKRBGkoeLYYDzJsytCa4CLLIzJi6qXeUW6+53c8jkZEFkMtN1nCjA8w6lNqI7JcNrvJTU/k1tTkKEyNeRiRiCRKyU2WyJscZCy/wuswRLJGVUkBtWWF9FwchtE++NP18Nw3vQ5LRBKg21JZ4kD5r1JSOcX/9joQkSxSt6ac5y8MQ+m1UFoLF5/zOiQRSYBabrJE9/QqRquu8ToMkaxy3doKnj8/5DxZfT1cetbbgEQkIUpussQvBP+FreOPeB2GSFa5dm05xy+NMBUKw6pr4aKSG5FMoOQmS7x+8ltsnnze6zBEsso1ayuYCllevDTitNz0HYdw2OuwRGQBSm6yRKkdwxRXeh2GSFa5dq3TSf+588NQ/07Yexx8+toUSXf6Lc0Coelpys0YPiU3IsuqpqyQVeWFPHt+CArLIL/I65BEJAFKbrLAyPAAAPklVR5HIpJ9rlkT1an4wK/Ajz/pbUAisqCUDwU3xuwBgu5Tv7W2dal1EiwHqAOw1jZHlTUAzcBhoAfYARyx1nYkfFBpZngixMHpN/CKVdd5HYpI1rl2bTnff+GS82SkF176AdzxgLdBici8UtpyE0kyrLXt1tp2oMsY07aUOgmUt1hrW91Hs7vtcNRb+IEGoM19dGdyYgMwZEv44+l3krfuRq9DEck616yt4MXeUSamQ7DuZjj/E69DEpEFpPq21D6gPfLEWtsJNC2xzpzlxhg/UO/+G9EGNBhjAlHbtlhrjbW2zk2QMtrowCXqzXNU5oe8DkUk69ywvoJQ2PL8+WFYezP0vuAsxSAiaStlyY2bTPittcE4ZQ3J1Elwn9uA6ESmx/3Xv4jwY/ddZIypjDyAtFrnwHfmKF8s+hiV4UGvQxHJOjesr8QY+OmZAaflxobhwtNehyUi80hln5vAHNuDzJ1oLFRn3nI36amOKYskPT1R23YZY/qAGqDOWrt3jv1G7AMeXOA1npkaDQJQWhl76CKyVKWF+QRWlfHTM4Nw203wi5+F2jqvwxKReXgxWiqSVCxnnfnK9wHNUa09XUCntbbDvSXVbYw5tMD77weqoh4bEw18JYRGBwlbQ1m5hoKLpMJNG6qc5KagGK6/H0r8XockIvNIuOXGGNMI7E7gpfuttV3zlC82sUmkTtxyY0wLcCC6X421tifmZQeBNmNM3Ntdbp0JYCJqv4nEvGLs+ADDpoRKX57XoYhkpZs2VNL59HlCYUte90Nw9jG457e9DktE5pBwcuOOKFrMqKLYJCLCP0/ZQnUS3qebjHXHdhg2xjRGj46y1gbdZCWA06qTccan4bxZg9ptRFLjpg1VjE6GOH5phKvPPwnf/wt49W9ptmKRNJWy30y3hSQYM0opUtaZTJ1E9xnpXBxJbIwxfmNMwB1FdSi6ftTIqrkSp7T3cO1uPlT5116HIZK1btrg/Onw0zMDsKEeJoehV2u5iaSrVP/ZsZ+XO/RGWlPao54HoibcS6hOAvusB+px5r8JuIlME9Dn3nZqjbk11QR0zHVLKhMMjU9TUZzy+RhFclZ1WSFX+EucfjcbbgMMnD7mdVgiMoeUXhGtta3GmD1uAgKwPXq2YJwkpRloTbTOfOVuK8xDOLepWmJjcf+7PyahqrXW7lzSgXrsbSf+lGkM8EqvQxHJWrdcUcXjJ4NQfAOsutZJbm77Ja/DEpE4Uv7nfszSCB0xZe3MbpVZsM585XMMBY+tGyQqmcoGVVPnmChMpp+2iCTq9iv9fKLzeaZDYfLv+nUo0dQLIulK9zKyQNH0MCNlV3odhkhWq7+qmrGpEM+eH+Kmre/yOhwRmYe6+meB4vAotjCtJk0WyTo3b6gi32d49EQQpifhqa9C/0tehyUicSi5yQIldhRbpORGJJVKCvO4YX0lXSf6nQ1feC88+w1vgxKRuJTcZIEPT3+UU5ve7HUYIlmv/ko/j50IQn6hMyT8xA+9DklE4lByk+Emp8M8Mn0Npnqz16GIZL3br6ym59II/SOTcNUr4aUfgLVehyUiMZTcZLjhoSB78z/H2skTXocikvW2XuWMkDryYh9c9SoYuQC93R5HJSKxlNxkuLH+83wg/9+pmb7odSgiWW9TTSlX+Ev4YU8fbLoDAvfC1IjXYYlIDA0Fz3Bjw07nxsJyzbkhshLuDNTySE8vFN8Iv/plr8MRkTjUcpPhJoaDABSX+z2NQyRX3FVXyzPnBgmOTjpDwk9pGQaRdKPkJsNNjg4AUFKhlhuRlXBnoAZrcW5NPf8t+NTroP9Fr8MSkShKbjJcMK+GA9OvpbxKyy+IrISN1aVsqinhhz29sOVuMHnQ/W2vwxKRKEpuMtypkuv5n+FmiopLvQ5FJGe8MrCK/3r+IhRXwcbt8MJDXockIlGU3GS48OA5biw8jzHG61BEcsZrr1tN98URTvaNwtX3wfHvQWja67BExKXkJsNdd+IAn+SPvA5DJKe86ppV5PsMDz97wUluqjbB0FmvwxIRl5KbTDc1wrhPt6REVlJlcQHbNlfz8LMX4Yqt8MEfgH+T12GJiEvJTYYzkyNM+Eq8DkMk59x73Rr+u/sS41MhZwkGzVQskjaU3GS4/OkRJvPUciOy0u69fg3jU2Ee6e51hoT/dT309Xgdloig5CbjhUIhJvIqvA5DJOdcs6acLavK+I+fnHXWmcorgme+7nVYIoKSm4z3f6v28a9XqkOxyEozxvCGm9fxrafOM5VfCnWvg6e/5nVYIoKSm4w3MjlNaZGWCBPxwhtvWU9wdMqZ0O+GN8HJH8HQea/DEsl5Sm4y3P8KPsi9/V/wOgyRnHTThkqurCnlG0+eg+veCJVXQO/zXoclkvOU3GS4QKiHcka8DkMkJxljuP/W9fzHT84yUVgFH/0JbH6112GJ5DwlNxmuxI5jisq9DkMkZ729/gqCo1M89PQFMMa5LTV80euwRHKakpsMZsNhShnHFCq5EfHK1WsquG2Tn45jpyAcgr+7C374t16HJZLTlNxksLHRIXzGkleioeAiXtq5bSMPP3uBC8NTcNNb4bHPQmjK67BEcpaSmww2PG143+T/YGzdz3gdikhOe/MrNlCY7+Pg0ZOw7T0wfE5z3oh4SMlNBhudzqMzvJX86o1ehyKS0yqLC3jr7Vfwrz98ialVN8CVd8GRT3kdlkjOUnKTwcb7TvHBvK9QGe73OhSRnPfuV27h/OAE//GTc3BHE5St0q0pEY+kfPY3Y8weIOg+9VtrW5daZ75yY4wf2OU+rQMCwAPW2mAi9TNJuO84ewoOcMJ+0OtQRHLedesqeGVdLf/4/eO8+YNvxdz8Nq9DEslZKW25cZMIrLXt1tp2oMsY07aUOgnsswXodMv3An3AoaXElK6mRgcBKC6r8jgSEQF4391beOxkkEd6eiE0DU8chIFTXoclknNSfVtqH9AeeWKt7QSallhnofIA0Bj1vBvYtsSY0tL0+BAAxeVKbkTSwb3XreHmKyr5q4eeh+kx+MbvwA/+2uuwRHJOypIbY0wA55ZPME5ZQzJ1EtmntXZHzG2m7UBnsjGls/CYk9yUlVV6HImIgDNj8W+87hp+2NPHj05Pwp0fgGP/BAOnvQ5NJKeksuUmMMf2IOBPss6i9mmMaXS3P7CEmDDGFBljKiMPIC0mlunNX83Xw68kL18LZ4qkix03ruXmKyrZ/x/PYO/8ABSWw8P7vQ5LJKd4MVqqD6hZ5jqzyo0xfmNME04ycyheS80i978PGIh6pMVN9Bcq7uDBwt/yOgwRiWKM4ffeeAOPnQzy78+OwD2/A499Bi5pQU2RlZLwn/xuK8juBF6631rbNU/5YhObROrMKneTmXYAY0yTMaYf2LKE/e8HPh71vII0SHBCw71cUTDsdRgiEuOVdavYceNaWv7jGRp+452UFpVDzVwNxyKy3BJObqy1HUDHIvbdM8d2/zxlC9WZt9wdBr4PJ8EKumWdbnkDMFfSNV9MWGsngInIc2PMXC9dUXeeaONNk48Cb/U6FBGJ8T/feAM/94nv8YmHT/B7b/wVZ+P4IBSrj5xIqqXstpS1tgcIup14Y8s6k6mTwD4DwB5mt8T43X+DycSUzszUKJO+Eq/DEJE4Nq8q4yMN1/Kp/+rhiVNBOPbP8P+2w0iv16GJZL1U97nZj9NiAszc2mqPeh6IzDuTaJ35yt3bYa1uEhOxG+iKSl4W2n/GyJ8eYSqv1OswRGQO77t7Czesr+SjBx5jdEsDhCbhax8Ba70OTSSrpTS5cYdk+40xjW4Ssd1a2xz1kgageTF1EtjnfmPMnsgDp+XmvkXUzxj5oVGm85XciKSrgjwff/mLt3MmOM4fPdwLb/oLePqrTgdjEUkZY/UXxKK5w8EHBgYGqKz07v75M39yJ0Nlm9n+kc97FoOILOzAkRPs/cKT/NnbbuEXz7Y6Mxe/rxPW3+p1aCIrbnBwkKqqKoAqa+1gKt5DE6RksI+U/Bmv2uxnu9eBiMi8dm3bxJOnB/j9L/+Eq961j7sqN0BtnddhiWQtrQqewYanwhSXFHsdhogswBjDx958E3fV1dL8uZ/wwk0fhsIyuPA0TI54HZ5I1lFyk8Faxz7GK4IZN8hLJCfl5/n4f79Uz/qqEt7xyR/xwtlL8G+N8Plfgqlxr8MTySpKbjLYVvsUVXbI6zBEJEFVJQV85oGfobaskF/8h0c5ee9fwIkfwqF3KcERWUZKbjLU1OQERWYKU1TudSgisgiryov47AN3srqimDd9FZ6652+h52H4t7fBaJ/X4YlkBSU3GWp02Olgnl+SFmt4isgi1JQV8vmmO7l1YxVv/mYJ39zajh04BUPnvA5NJCsouclQYyMDAOQXK7kRyURVJQV8+t3beeedV/H+7+bzG6s/xUDFNU4H4+cPex2eSEZTcpOhhinjo5MfwK65yetQRCRJ+Xk+PvbzN/E3v1TPd5/v5/V/+T2e/c92+EwjfO23nLWoRGTRlNxkqGFK+FL4bgqrN3gdiogs0f23ruc/P3oPV68p5+f++xo+U/sbhB/7LPztnfDMN7Rcg8giKbnJUNO9L/EreYcpf3mxchHJYOurSviX99zBX7+jnr8aeg33jbXwvN0En3+HMx+OiCRMMxRnqLwLT/LHBZ+mL+93vA5FRJaJMYY3v2IDr7t+Df/0gxdp/O46rpl+lpt+GOZddw0SePz/wE1vhQ23ex2qSFpTcpOhQuPO/DalFX5vAxGRZVdWlM+v33s1v3rXVfzj9wP88yMv0vnIUb5UdoA1//2XhK98Jb67fh2ufT3k6WtcJJZ+KzJUaHyYaeujqKjE61BEJEUqigv4zYZraH5NgK89cZYH/nsL6859m/ef+A9uP/HLDK3eStkHHsLnM16HKpJWlNxkKDsxxKgpodKnblMi2a64II/GrRt5e/0VPHPudr7y2Dv4y0e/jz19gaf3P8TbN0/xgQsPUnzzz1N405tg/SvAKOGR3KXkJkP15q3mR77b2OF1ICKyYowx3LC+khvWVxL+ues4dqKfb/30HM889SjfHqzldd//Gwr/+88ZKlrL4PW7WfXmj1GUn+d12CIrTslNhuqqvI+Hy25RciOSo3w+w/bNNWzfXAP338jJvrfyladPc/bxh9hw/mEGj73IJ459i7s3wJ+OPMjkplfjv+FeKuruhPLVXocvklJKbjLU+FAfq9TdRkRcm2pKeeerroFXXUMo3MzTZwcpOt5H93M/5ciFtWx9+ktUPPMPAJwsrONrdx3k1k1+bi04Q8X6Oigs8/gIRJaPkpsMteuljxH2FQKv9ToUEUkzeT7DzVdUcfMVVfDqLVh7P8cvDvOtF55m+IUf0n/pLP/vOy8wOTnBT4reS9hMcyF/Pf3l1xJafSPT25uou3IjFcUFXh+KSFKU3GSo0ql++qpu9DoMEckAxhgCayoIrLkDXnkHAO8OW45fGOQHT3+WsZOPU3DpaWoGn2Nz/79y75O3M8hTfKr0/3G97xQDJZuYrNpC3qqrKbz2PtZvvoGqUiU+kr6U3GSoilCQS6WrvA5DRDJUns9w9boqrl73s8DPzmwfn5zmsxdHeP7CEGNPvZ4XLj5KxcgJVg8dZsPJz/I/fnyBr4RfzbuKv8sHzJcIFq5lrHgtU+UbGF9TT+i6+1lbXsA600tl7Tryi8u9O0jJWUpuluDH//y7lJUUARDyFfKjje8B4PYzn6dkOjjrtU+vfgP9JVexcaCLKwd+PKust7SOZ1ftoGh6iK1nPkNebYBbfvbdlJTFX/HbhsNU2wFeLFOnQBFZXsWF+S/f0rr9N2eVDQyN8EDfCDsGphl/cZLnTo9SNHKGstFzrB14gkdOHOd/PLKG9fTySPGHARiliKDxM5zv5+NXfILK8jLuG/4q1XYQU1yJKa4ir6SSqXX1FNRsotKMUcYopSVlFBaXUFhUiq+g0IuPYsVYawlbCIfDhG0Yi49QKER4aoxwOIwNhwmHQk55sZ+QtTByCTs9iQ2728MhpktWEcovhZFefKMXsNa65SGmCiqZKN+EnZ6g+OKTYJ39WuvU71+9nTB5lPc+Tv54n1sewtowwYrrGS69gpLhk/j7n4DwtLPemQ0zXljD6dV3gw1xzYkOIAw27K6HZnlu/VuYzC9n48Xv4R85jgUuTaW+w6iSmyW46sw3qShy5pkZMSV88cLPAfCaycNsDJ+b9drPnb+KY3n5NE4f5TXT/z6r7EzeVr544kZW2V4aJ77KmpO9DD++n8cC76V+1+9SVDy7o9/wUJAKM0VB5ZoUHp2IyGxVFWVUVZRxM8CtbwXeOqv8LaEwrxye4HxvP0df/EemB88TGroIIxfxjQeZtPk8d36YN/b9N5tDz1BmRykzzvp4vzH5Ib4afiXvzvsmHyv4l1n7/U74Nj7EPmryxviC/S0mTSEhU4DFR9j4+J9VLYzllfOe4U9y7fSzhPFhjfM4XP4WukpfxY0Tj/Omwc8DFuMuRHo2fyP/WPUhwtbyB72/g8+GnXIsWPjzyr1c9K3mF0c+w7bJIxjCGCw+G+brhT/HlwreyE3TT7Nn/K8whPERxtgwF8wq3pv3J1hrORj6CDUM4LNuXSzvDe2jy17Db/s+y3t838BHmDxjyQP+bfo+fn/6vdxoXuQbRb8363MYsUXcNPFpAP6zcA/X+U7NKm+a/CjfCm/n/Xlf5XcLPj+r7BuhO/jg1EdYSx8/Kv7QZef2uvF/YoJCPlvwx2zLe2pW2e9OvY/Ph17HzryH+fOC9lllPw5fz4PTNRQyzRMFf+Z+egbrfpL7ntrMObOKPzVf4FX8GAscndwU78drWRmr1WYXzRhTCQwMDAxQWVm57Ps/c/wZTn69la0Xv8SRvNsZeNtn+Lmb1mHcSblevDjM/f/3m3zqPXdx17VXLPv7i4islMnJSUaHggyH8hgKFTDV+xL20nNMTYxhp8YJT40zlF/Ni/67CE+OcOuL/4yZHseGnFYLY8N8Y/0HmKCYV108wLrxbgzOdmPDPFL5czxVup3AyBPcO/AFwIBx0ozewg18ffUDGAO/cnY/YAGfOwGi4T/XNTFSWMvW/m9w5chPsSbPKTM+uqvu4nj1K6mdOMltF/99ZjvGx0RBFY9v/GXyfIZXnP4s+eFJTFTdl9b9LBOl61gdfJyaoecwPuOU+/IYrtjCwKp6CqcGWXvhvzDG5zx8PvAV0LupAZ8xVF88Qv70GCbPByYP48tjouY6wqWrKBo9R9HIaYwv36mb54NiP6GqzfjCUxQNvogxPnx5PowvD5/xYf1X4svLI2+sD5+dwpeXN1PmKyrHV1CMLzyFz05jfD6ML985XgwscjLZwcFBqqqqAKqstYPL/TMFSm6SkurkJuKlZ7r4p+88yadfWsVbNw7SfMcqrr9jB8de6uPtf/cI3/roPVy7Nv6tKxERkXSk5CZNrVRyE/HwsxcY/OJH+fmJr/Fo6asYXvUK7PH/4qaPfoXamtqUv7+IiMhyWYnkRgsTZYDXXreGN+35F47Wt7B27AXuPvG3VFRUUOOv9jo0ERGRtKOWmySsdMtNtKnJCZ47+hDXbruPgsKiFX1vERGRpVqJlhuNlsowBYVF3PTKN3odhoiISNrSbSkRERHJKilvuTHG7AGC7lO/tbZ1qXXmKzfG+IFd7tM6IAA8YK0NuuUNQDNwGOgBdgBHrLUdizsyERERSUcpbblxkxCste3W2nagyxjTtpQ6CeyzBeh0y/cCfcChqHI/0AC0uY9uJTYiIiLZI6Udio0x/cCWSKuJu81aa02ydRIoPwwcjrTmuMnQPmtttfu8ESf5mamfxHF51qFYREQkk2V0h2JjTADnllEwTlmDtbZzsXVwbiPNu09r7Y6You3AZe+1GMaYIiB6aJJmzhMREUlTqexzE5hjexDn1lAydRa1T7eVxg/sjCnaZYzpA2qAOvf21Xz2AQ8u8BoRERFJA14MBY8kFcnUCSayz6hOxX7gUExLTxeAtbbHfW2TMeaQtTY2AYq2H/h41PMK4NQcrxUREREPJZzcuK0guxN46X5rbdc85YtNbBKpM6vcTWbaYSZ5memnE0lqohwE2owxcW93ufubACYizyMLWIqIiEj6STi5cUcULWZUUWwSEeGfp2yhOvOWuy02+3ASrKBb1snLI6Q6jDGN0aOjrLVBN1kJ4LbqiIiISOZK2VBwt4Uk6HYSji2L28F3oToJ7DMA7GF2S47f/TfoJj+Houu722DuxElEREQySKr73OzHaTGJ3CJqjPzffR4AGmMm6Zu3znzl1touY0xrzK2n3UBXJKGKU94EdCQzNHxwMCUj2ERERLLWSlw7U75wpjvPTCSZ2B49MskY0wTstdbWJVongX36cRKWiDr3PYJzlNcmMFoq9pg2A8cXU0dERERm2WKtfTEVO9aq4EmITOIHbASGPA4nlSKjwnSc2UHHmV1y5Tghd441144z8ybxyxFDqTox6SBqVJiOMwvoOLNLrhwn5M6x5uBxpoxWBRcREZGsouRGREREsoqSm+RMAH9I1MR+WUrHmV10nNklV44TcudYdZzLRB2KRUREJKuo5UZERESyipIbERERySpKbkREslzUMjMZIxNjXin6bBam5EaSlom/YJkY80pJ58/GGNNgjDk0R1lT1GNPgvtbdJ2VsMBx7nEfh4wxLQnuyxpjLNBvjOmOty6fF+Y6zmRjzrTz6Z5DG+dxbIF9peX5TEeaxC8Od1mICH/M2lfLVicdRH0RbAd6FlqKwhjTAByOet4D7IhZryutJBtzpp1T90u0MU5Rl7V26xx10vp8uvHtwFkA97IvcvcczZwbY0yjMaZlvp/jZOqkWgLH2RKzzMwhY8wha+3OeXbrByLnPZgO53Sh4ySJmDPxfAJ9bnlf1LbdwIF5dusnzc5nxELXEU+uqdZaPaIeOOtO7Yl63gi0LHeddHjExggcAg4tUKcRqHcfAa+PIcHjXHTMmXhOgTacRWXrox4tQH2mn083zmNxtnfHxg30L7CvRdfx8jhxLmrH3C/4yLZ6wM53ztx9peU5ned8LjrmTDufke1xzvGeBPaVdudzoeuIV9dUzz+YdHtk25flPDFn3Rfmcsacoec0a74w54gz3kXfxnmtnSuhS6ZOmhxnf3R8keMAGjLx3C5XcpOJ53OO1y144U7H85nIdcSra6r63ERx+xwE7OXNfX5jTP1y1UkjAWY3mfZEbc9ZmXpOrbUdMZv22TS/lbZEc/2cBucpS6aOp6y1QWtttbW2K2pzJNaFbk00uLdpmhLpp5MmFhNzxp3PWO53ypEEX56O53PO64iX11T1uZltoV+UrjhlydTxnLU2CFTHbF7MF2YfUAPUWQ/vbS/CYmLOyHMaLYkvzEw7n+DEG0/fPGXJ1ElHzUBnnAtAtB6c/g9dMNPpts1a27wiESZnsTFnw/ncZ+fvOxWRduczgeuIZ9dUtdzMlstflpD4F+ZRa22HtbYd6DbGtK1MeElbbMzZcE73xWnJiScTz2dOcxPXBmDeC6K1tiumtacTaErnUXGZGPNSLGa0UwZ9NtHXEc+uqUpuBNAXZjbJ0i/MePrm2F4zT1kyddJNC7DV/as5YVF/tGTE7RpIKOZMP5+NLNxSHlc6ns9EryMrQcnNbLn6ZQn6woyW6ec0q74w59EDcefn8TP38SdTJ224rWrNC/2eGmP8xpj+6EQ33RPWJGPO6POJM/y7e6EXZdD5jL2OeHZNVXIzW859WYK+MOPI9HOabV+Ycbk/r3GbvmNao5ZUJ1248360RBJQY0z9Ah0sj8bcYg5A2h/nomLO5PPpCpD4BTutz+cc1xHPrqlKbqLk2pcl6Asz3osz/ZySRV+YUea6196Cc48fmPl5jp7sLhAzGdiCdTwW9ziNMY24E8K5M9U24hxD5Pd21nG6P8OHY3azjzQ+zkRizpbzGcWP01F2lkw7n3NdR7y8php3/Li43JM0M2IkzuyXAZy5JdoTrZOu3C/I6N7nfpxZM/daa4NzHOue6OMyzqy4R9L5WBeKOZvOKYBxpmffYa3tjNmecefTTbR3487xAbTjzBsy6xhwLhB+oNbOnsm3CefnuS5mv3PW8cJ8x+n+Bdsfr5611rj15ztOgDpiPjcvLOJ8QpyYs+F8xryuH7gv9qKdKecTErqOeHJNVXITRzZ8WS4km74wE5ENX5iJyoYvTBFJf4lcR9zXrfg1VcmNiIiIZBX1uREREZGsouRGREREsoqSGxEREckqSm5EREQkqyi5ERERkayi5EZERESyipIbERGXuyTFMWPMofmWojDGNLmv2TPXa0TEO5rnRkRSyp2BtBtnxfHIxIJ7cKZY78CZpGsbELDWVrt1WtznK7q6sJvQ7Et0wjBjTJu1tnnhV4rISsr3OgARyXp+nBlIo5d5aAA6Y2YqPRRV5wjQu2IRikhWUXIjIqkWwGmhWcjMwoDW2kReLyISl/rciEjKxaw8Ppee+fq5iIgkSi03IpJSibbCRFYxd1dU/qS7bau7rQFocV/6AE5rUA2w1Vrb7C6+14ezEvP+OIuGtuDc6goAwcUsDup2Go4kZ4uuLyIrT8mNiKQVa22XMWYv0Ba1rTNqW00kYTLGdBtjWiJ9d4wx4CRGWyN1jTGHgZao5OmQMaYn8nw+xphGnGQm8n4BoHGZDlVEUkS3pUQkHfXNsS0Qk5TE3u7qwWldAWZagRpi6hwGEh3hVAPsjNwuc2+vLZgUiYi31HIjIpkkGGdb9zzl23D68kS3tvhxblEtyFrbbozZCfQbY7qAA9GjvkQkPSm5EZGst5TRV9baHe7tqAZgrzGmNtF5cETEG7otJSLZ7ChRt6ki3GRlQe5MxH5rbY/biXgrTpIjImlMyY2IeMHvPlKx3xnuqKkOdzQVMDMLcf0i9tcUsy2RYe0i4iHdlhKRFRNZVsF97HJHNx2LHlrtdgJuAQKR5Q2itvmNMW3AXmAfTitKwBjTh5N0zHqNtTZord1pjGlxh3QHwelLk2DIQTemSJ+dAM5QdBFJY1pbSkTEpbWlRLKDbkuJiIhIVlFyIyIiIllFyY2IiIhkFSU3IiKzNbhLNPjneoE7RPwQsycQFJE0oQ7FIiIiklXUciMiIiJZRcmNiIiIZBUlNyIiIpJVlNyIiIhIVlFyIyIiIllFyY2IiIhkFSU3IiIiklWU3IiIiEhW+f/AgsiDlkODNQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(<Figure size 600x400 with 1 Axes>,\n",
+       " <AxesSubplot:title={'center':'PVD1 Iq\\\\_out (actual output current)'}, xlabel='Time [s]'>)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Cleanup"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "source": [
+    "!andes misc -C"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "\n",
+      "    _           _         | Version 1.3.12.post61+g506b9a1c\n",
+      "   /_\\  _ _  __| |___ ___ | Python 3.7.10 on Linux, 09/01/2021 10:05:58 PM\n",
+      "  / _ \\| ' \\/ _` / -_|_-< | \n",
+      " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\n",
+      "\n",
+      "No output file found in the working directory.\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "22e39ba7dda51165b7745563f4e2534bee0d38fdb3ee17f5faee115ef7f334ee"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.10 64-bit (conda)"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "7dcf7be8cc97f6f28812ee601415372b88ddefbec529c6a68a6def4717df442e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/examples/demonstration/1.2 demo_DGPRCTExt.ipynb b/examples/demonstration/1.2 demo_DGPRCTExt.ipynb
new file mode 100644
index 000000000..c0165f3bd
--- /dev/null
+++ b/examples/demonstration/1.2 demo_DGPRCTExt.ipynb
@@ -0,0 +1,548 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ANDES Demonstration of `DGPRCTExt` on IEEE 14-Bus System\n",
+    "\n",
+    "Prepared by Jinning Wang. Last revised 2 September 2021.\n",
+    "\n",
+    "## Background\n",
+    "\n",
+    "Voltage signal is set manually to demonstrate ANDES `DGPRCTExt`.\n",
+    "\n",
+    "In the modified IEEE 14-bus system, 10 `PVD1` are conencted to `Bus4`, and 1 `DGPRCTExt` is added aiming at `PVD1_2`.\n",
+    "\n",
+    "## Conclusion\n",
+    "\n",
+    "`DGPRCTExt` can be used to implement protection on `DG` models, where the voltage signal can be read from outside."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import andes\n",
+    "from andes.utils.paths import get_case\n",
+    "\n",
+    "andes.config_logger(stream_level=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ss = andes.load(get_case('ieee14/ieee14_dgprctext.xlsx'), setup=False,\n",
+    "                no_output=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.setup()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use constant power model for PQ\n",
+    "ss.PQ.config.p2p = 1\n",
+    "ss.PQ.config.q2q = 1\n",
+    "ss.PQ.config.p2z = 0\n",
+    "ss.PQ.config.q2z = 0\n",
+    "\n",
+    "# turn off under-voltage PQ-to-Z conversion\n",
+    "ss.PQ.pq2z = 0\n",
+    "ss.PFlow.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TGOV1.LAG_lim at limits <VMIN>\n",
+      "\n",
+      "  idx    | Flag | Input Value | Limit\n",
+      "---------+------+-------------+------\n",
+      " TGOV1_2 | zl   | 0.300       | 0.300\n",
+      " TGOV1_3 | zl   | 0.300       | 0.300\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████| 100/100 [00:00<00:00, 485.57%/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.config.tf = 1\n",
+    "ss.TDS.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "At t=1s, Bus4 voltage is: 1.01\n",
+      "\n",
+      "Now set the external voltage at 0.7 manually.\n",
+      "\n",
+      "Now continue the TDS to 5s.\n",
+      "100%|█████████████████████████████| 100.0/100 [00:00<00:00, 156.66%/s]\n",
+      "\n",
+      "Now reset the external voltage back to normal manually.\n",
+      "\n",
+      "Now continue the TDS to 10s.\n",
+      "100%|██████████████████████████████| 100.0/100 [00:01<00:00, 92.07%/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Store initial Bus4voltage value\n",
+    "bus4v0 = ss.Bus.v.v[3]\n",
+    "\n",
+    "print('At t=1s, Bus4 voltage is:', ss.Bus.v.v[3])\n",
+    "\n",
+    "print('\\nNow set the external voltage at 0.7 manually.')\n",
+    "ss.DGPRCTExt.set(src='v', idx='DGPRCTExt_1', attr='v', value=0.7)\n",
+    "\n",
+    "print('\\nNow continue the TDS to 5s.')\n",
+    "ss.TDS.config.tf = 5\n",
+    "ss.TDS.run()\n",
+    "\n",
+    "print('\\nNow reset the external voltage back to normal manually.')\n",
+    "ss.DGPRCTExt.set(src='v', idx='DGPRCTExt_1', attr='v', value=bus4v0)\n",
+    "\n",
+    "print('\\nNow continue the TDS to 10s.')\n",
+    "ss.TDS.config.tf = 10\n",
+    "ss.TDS.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Results\n",
+    "\n",
+    "### Lock flag\n",
+    "\n",
+    "The lock flag is raised at after `TVl1` when the voltage drop below `Vl1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 600x400 with 1 Axes>,\n",
+       " <AxesSubplot:title={'center':'DGPRCTExt\\\\_1 lock flag (applied on PVD1\\\\_2)'}, xlabel='Time [s]'>)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.plt.plot(ss.DGPRCTExt.ue,\n",
+    "                title='DGPRCTExt\\_1 lock flag (applied on PVD1\\_2)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PVD1_2 read frequency and frequency signal source\n",
+    "\n",
+    "The `PVD1_2` read frequency is locked, but the signal source (in the `BusFreq 4`) remains unchanged"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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YlpeuurSwW5qbjvssRbjPUgTnjMTbZ+146VgfXjreh79tOYQkIbC1xISPbFiGuzYsjYouqljAQBKE7r5hfPz7r6ouAwDw7FdvwfWFeUEdQ//wjzcdHR2zM4cA1we0rw9ms9k826Khtxp5ju2IpPLycr9CRHNzM6qrq9Ha2gqTyRSXP0eKDp2nB/F/2o7jlff7YV6chYaqG/DJTSuQnhIfrSGBMiQJbF6Vj82r8vFXO9bi0uVxtB65iOcPX8Q/P3sE33jmMDatNOKjG5bh4zcsx0pTpuqSoxYDSRBKF2fj2a/eoroMAK5agtXd3e11emtXVxcefvhhlJeXz06BbWhowJ49e/Doo4+GpWvHbrcDwGw9vgbZtrW1oaurC42NjbDZbNi9e/dVH+Dl5eV+Deb0ZLFYsHv3blRXV2NwcPCKbWazed5Bv1ardc7pwoGqra2dPS8AZruzAFfIqqysRHV19VWDb4lCobtvGA3PvYfnD1/EuqU5+P5nNuPujcthSOL6He6W5KTjga2r8cDW1XCMTuGFYxfx/LsX8a/7jqP+ufdgWWXEvTcW4p4blmNRNgfFumMgCUJGqiHoVolo0tXV5XVAqz6A0n09DovFgvLy8rCNM+no6ADgChOA6wPX2yBL/YNY79IIdZeEPtDUM1xUVVXNO+izra1tQUForlrcebZolZWVzQa0+VpIQh2WKH7ZRyfxnT8cx5MHzmBZbjr+5f5NuHdTIZIYROaVl5mCT20uwqc2F2FkYhptRy/i1wfP45+fPYJvPnsE29cswr2bVuCuDUu5kix4LRty46vLxn12h661tXVBU0/91djYiPr6+tmxJPqCZ75CQCAzThaiq6vrivt6PfqCZt7qCWTWz0Lqcf956AGjsbHRry4mz++HyNPMjMTe9jO443+/hF+9dQ5f/+i12Pe12/GpzUUMIwuQlZaMe28sxI8eqkD731fim/duwPiUE19rPoTy/9GGrzzRhRffu4RpbbG2RMRAQrPsdrvXv5rb2tpgMpnQ0tJyxc190GZXV9fsYMqWlha/p5h609DQALvdflVrR2trK/bs2XNVCAjVehk2m+2q45jNZhiNRrS3twPAFWNRmpubUVdX57WepqamOVuP7Hb7Fd0vvurxxXP1WH09FP3m2dJlNptnfyZWq9WvMSZcgyRxnbg0jKr/eB11v3gHd6xbgn1/czsevs2csONEQi0/KxUPbF2Np2q34fWvfxhfu2stuvuG8YUft2N7/Quof+49WKNkwkQkCSnl/M+KY0KIXAAOh8OB3Nzcq7aPj4/j5MmTKCkpQXp6/I4e18eJeBt/UFtbi7Kysiu6H4QQcP+/o/+F3tXVBZvNNucMk/mWai8tLZ2z68VzqXbANdvEfWqwPltmz549s8uv19TUeG1F0evRA4K+dL3+3La2NtTX16O2thZGo/Gq781XPd7oM2bcv3f3gbRznR/gg6XjrVYrpJRoaGhAY2Pj7DbAtYZLS0sLampqrphto9c539L8+rnbu3cvurq6sGvXLlRUVMwZsBLl9yTeOWck/vMVK77TehyFxgzsuW/jFetxUPhIKfHuuSE0d/bgV2+dw9D4NMpX56O6vAj33LAC2WnROcJiaGgIeXl5AJAnpRwK5lgMJAwkAFx/+Xd2ds5+uLkrKyvDo48+OvvB5iu86NdY4diExJMovyfxrMc2ir/8+Vs42GPHn99Sgq/dtY4tIoqMTznReuQinurowasn+pGebMDdG5ejurwIW0tMUXUhwFAGEnbZJDh9LEFra6vPsQeegyS9LVKmL7hlMpkCWqCMiNT7zaHzuPtfX0H/8ARavrQNf3/PdQwjCqWnGPCJTSvw0y9uxWt1H8aXP1SK9lM2fLrpTdz53T/iP1+xwj46qbrMkGMgSXBlZWWz4xk8Q4b7zBo9uLS1tWHv3r1XjIHQA0praytKSkrCOtiViEJnfMqJr//ibXz1Z2/h9nWL8du/vBVlq8M3GJsCt8KYga/eeQ3++Lcfws8evgkbVuSh/rn3sOVb+/DXTx1E5+lBxEtPx4K6bIQQ9QD0RRhsUsoWt227ANi1u0YpZYMfx5tzHz+2GwHsBFAtpQzo0zDRu2z0lo3KykoupkULFu+/J/HovH0MtT/txPGLl/HNezdgZ/nKqOoKIN/6hyfQ0nkWT+4/gzO2UVy7LAcPbF2FP9lcGPHpw8rGkGgf/PsA3CmltAshLAA6pZRC274LAPTAIISohCsk+Lxa23z7+LHdAqAcgBHA/VLKMr+/ITCQEIUCf09iy5vWAXzliS6kpxjQ+LmyuFpPKZHMzEi8eqIfT+w/jbajl5CWnIR7b1yBz25ZjY1FkfmZqgwkjQC63VsohBCVUso27etBACVSSrvbdqkHFh/HnHMff48phKgCsJuBhCjy+HsSO57q6MHfPf0Oyovz8YPPWlDA1ULjwsWhcext78HPDpxBr2Mcm4ry8Lltxfj4DcvDOh5I5aDWGgAtQgiz1lIBtzBihqs7xe65k/5cL4/Puc9CjklERFeTUuJfWo9jV8vbqC4vwk+/uJVhJI4szU3HX955DV7ZdQf+88FyGDNT8TfNh3Dzt19Aw3Pv4bx9THWJ8/J7YrMWDgDAAsAKwKq1mDRrocTXXE87XN0p3sy3z0KOOSchRBoA99/CHH/2i5dBQ0ThwN+P6DblnMHup99BS+dZ/O1H1uHLHyrleJE4lWxIQuV1S1F53VJY+4bx0zdP46dvnEbjy1bcdd1SPLitGDeZo2vqsC6QlVb0cGCXUnYBgBCiDsBJAPlz7GcDEOiwbX0fewiPqdsN4Bv+PjklJQVCCIyMjCAjg5eQJvJmZGQEQgikpPB6HNFmfMqJv3iyC3883of/c/+N+JPNhapLoggxL87GNz6xAV+7ax1+2XUWP3njND7z6Ju4dlkOHtxWjD/ZvAKZqdGz4NpCKunQv9AGthrn6T5ZSHCYb59g5qXtAfBdt/s5AM76erLBYEBeXh76+vowMTGB3NxcJCcnR2W6JIokKSWmp6cxNDSEoaEhGI1GGAxcuyKajExMo+anHeg4NYhHHyzHh9YtUV0SKZCdlozPbSvG/3PTarzePYAfv34K//Crd/Dt3x/FzvKVeHBbMVYVZKouM6BA4uviJHa4Wk+8X2XM1bXia19fj+v7zLc9YFLKCQAT+n1/gsWyZcuQkZGBS5cuYWgoqDE7RHHHYDBg+fLl+sA2ihJD41P4wn+1473eIfzkz7ZwCXiCEALb1yzC9jWL0GMbxeP7T2Nvew9++NpJfHjdEnz+5mLcsmaRsosnBjrLphuuKbddbo9JAGVSyi5tRkyZlNLqvt2PWTY+9/H3mOGaZeNOSgmn04np6elAXoIobiUnJ8NgMLDFMMoMT0zjcz/cj+5Lw3jsi1tx40qj6pIoSo1POfHMwfP48euncKR3COZFWXhw22r8aVmRX2uaqJz2WwWgQkpZ53a/Vl+MTF/ATErZ5LZ9h9uaIWYAVR7ThufbZ87tbsep0WoJWyAhIop2o5PTeOhH7TjaO4QnHt6KG4qMqkuiGCClRMfpQfzk9VN47t0LSE8xoKqsCJ+/uRgli7J87qf04nraB79Ru1ughxO37bvwQXdKhft2bd86KWWpv/v4cUwzgCoA98M1A6gBQLv76rHzfD8MJEQUF8annPizH7fjUI8dj31xK8pWzzXfgMi7C45xPLH/NJ7cfwYDI5O4Y91iPLS9BLd66c7h1X5DiIGEiOLBtHMGX3q8C6+e6MNPvrAFWzlmhII0PuXEs2/34r9eO4nD54dQujgLD91cjPssRchKcw1BZSAJIQYSIop1Ukp8/Rfv4BddZ/Ho58txB2fTUAjp3Tk/fu0Unjt8AZkpBuysWInPbyuGMWU6ZIEkeiYgExHRgny39Tj2dvTgO9WbGEYo5IQQqCg2oaLYhHP2MTz+5mn87MAZ/Oi1k7h1te/xJYEKdOl4IiKKIk/sP43vv3ACuz92Lf60rEh1ORTnCo0ZqPvotXhz95349n0bQ7okPbts2GVDRDHqlff78NB/teNzN63GNz5xHadfU8Q5HA4YjUZAwcX1iIgoCpy4NIwvP9GFW9Yswj/cs55hhJQI5f87BhIiohhjG5nEF3/SjmW56fj+Zzcj2cC3cop9HNRKRBRDpp0z+MoTXbg8Po1ff2U7cv1YTZMoFjCQEBHFkP/1/DEcOGXD41/cipUm9RdEIwoVtvMREcWI37/Ti8aXrfj6R6/FtlIufEbxhYGEiCgGnLh0GX/TfAj33LAcf35riepyiEKOgYSIKMqNTTrx3x7vwgpjBhr+9AbOqKG4xDEkRERR7pvPHkHP4Ch+8xe3zF5DhCjesIWEiCiK/fbtXvzswBl84xMbcM3SHNXlEIUNAwkRUZTqsY3i60+/jXs2LsenK1aqLocorBhIiIiikHNG4v/bexC56Sn41n0bOW6E4h47I4mIotCjr1jRdWYQzbXbkJfBxc8o/rGFhIgoyhy/eBnf/cNxPHyrGeXFJtXlEEUEAwkRURSZcs7ga08dwqqCTPz1jrWqyyGKGHbZEBFFkX97sRtHeofwyy/fjPQUg+pyiCKGLSRERFHi+MXLeOTF9/Hfbi/FDUVG1eUQRRQDCRFRFJiZkfj7X76DlfmZ+Oqda1SXQxRx7LIhIooCzZ09aD81iCcf3oq0ZHbVUOJhCwkRkWL9wxP41u/ew32WQtxcukh1OURKMJAQESn2rd8ehRDA39+9XnUpRMowkBARKdRxyoan3zqH3R+7FgXZaarLIVKGgYSISJGZGYlvPnsEGwvzUF3Ga9VQYmMgISJS5FcHz+Htsw7848evQ1ISr1VDiY2BhIhIgdHJaTQ8dwwfu34ZtpRweXgiBhIiIgWaXrbCNjKJ3R/jQFYigIGEiCjiLg2No/GPVnxhezFWFWSqLocoKjCQEBFF2L+91I0Ug8CX7+CKrEQ6BhIiogjqdYzhyf1nUHObGXkZKarLIYoaDCRERBH0yAsnkJVmwEPbS1SXQhRVGEiIiCKkxzaKpzp6UHt7KbLTeCkxIncMJEREEfLICyeQl5GCB7etVl0KUdRhICEiioAzA6No6TqLL91eisxUto4QeWIgISKKgKZXumHMSMEDW9k6QuQNAwkRUZj1D0+gueMsHrq5GBmpBtXlEEUlBhIiojD7yeunYEgS+BzHjhD5xEBCRBRGIxPTeOyN0/h0xSoYM1NVl0MUtRhIiIjC6OftPRiZmMYXb+W6I0RzWdBQbyFEPYBu7a5NStnitq3G7alGKWWDH8ebc59gtxMRqTDlnMEPX7Hik5tWoNCYobocoqgWcAuJEKIVQKOUsglAB4Bmt201cAWCJm27VQsvcx1vzn2C3U5EpMqzb5/Hecc4am43qy6FKOoJKaX/T3Z9+JdKKevcHrNIKbu0r7sB7JBSWt22D0op8+c45pz7BLvdj+8pF4DD4XAgNzfXn12IiOYlpcQnHnkVpqw0PPZnW1SXQxQWQ0NDyMvLA4A8KeVQMMcKtIWkHkCr+wNuYcQIwOweDDRGIYTF28Hm2yfY7X5+T0REIddxehDvnhvCn20vVl0KUUzwewyJ9uFvhOvDXh+z4d5a4qtN0q5t6/Kybb59fPF3u7fX9MoxNgWZMuXv0ynBCAHkpCVDCKG6FIoRP3r1JEoXZ+G2axarLoUoJgQyqFUPACZtrAaEEJVCiGYpZTUAk4/9bHNsm28fe5DbryKESAOQ5vZQDgBs//YLSErL9HE4ImD3x65F7e2lqsugGNBjG8Xzhy/gm/dej6QkhlgifwQSSPQP+A79ASllmxCiVQgRSyO2dgP4hueD/7JzEzKzcxSUQ7HgP/7YjfZTNgYS8stjb5xCTnoK7rMUqi6FKGYEEkisHv+6s/h4HHAFGZuPbb4e1/cJdrs3ewB81+1+DoCzOzYs46BW8umtHjt+/26v6jIoBgxPTOPn7T14YOtqXkSPKAB+D2p1GzjqqzXECsyONXFnhO+wMt8+wW6/ipRyQko5pN8AXPZRG9GstUtz0GMbw/DEtOpSKMr9sussRiedeJDLxBMFJNBZNl3wHki6pJR2uELAVWM39Jk4Xh6fc59gt/v+NogCs26pqzvv/YvMr+SblBJPHujBndcuwQouhEYUkEADSR2AHfodbbZNi1vrST2AWo/t7muWmD1WVZ13nxBsJwraNUuzIQRw7AIDCfn29lkHjvYO4TNbVqkuhSjmBNTBqQ1iNbuvhKrNsNG/bhJC7NJXTwVQ4L6IGoBKuMJCk7/7BLudKBTSUwwoLsjCMbaQ0Bx+duAMVuSl47a1nOpLFKiAR1zpU37n2O7zOjLavlftP9+1Z4LdThQK65bmsIWEfBqemMYzh86j5jYzDJzqSxQwXu2XyE9rl+XgOFtIyIffHDqP8SkndpavVF0KUUxiICHy09ql2egfnsTgyKTqUigK/fzAGdy+djEHsxItEAMJkZ+W5qYDAAZGJhRXQtHmyPkhHDrrwKc5mJVowRhIiPyUn5kKALCN8JpHdKWnOnqwOCcNH752iepSiGIWAwmRn0xZeiBhlw19YNo5g2ffPo97N61AioFvqUQLxd8eIj/lZaRACGBwlIGEPvB69wD6hydx7428bg1RMBhIiPxkSBIwZqSwhYSu8KuD52BelIXrC3ktLKJgMJAQBSA/K5WzbGjW+JQTz797AffeWAghuPYIUTAYSIgCYMpMhY1dNqTZd/QSRiad+OSNK1SXQhTzGEiIAsAWEnL364PnsKkoDyWLslSXQhTzGEiIAlCQlQrbKKf9EuAYncJLx/rwSQ5mJQoJBhKiALCFhHS/f7cX0zMz+MQNy1WXQhQXGEiIAmDKZCAhl2ff7sVN5gIs0VbwJaLgMJAQBSA/KxWXJ6YxOT2juhRSyDE6hTetA/jY9ctUl0IUNxhIiAJgykoBANg50yah7XvvIqZnJHZcx0BCFCoMJEQBmL2eDQNJQnv+8AXcuNKIZXnsriEKFQYSogDwejY0NunEH4/34aPsriEKKQYSogDka4FkkFf8TVgvv9+H8akZfGQDAwlRKDGQEAUgJy0ZKQbBLpsE9vzhC1i7NJuLoRGFGAMJUQCEEMjn1N+ENeWcwb6jl9g6QhQGDCREATJlpaLv8oTqMkiBAydtcIxNMZAQhQEDCVGACo0ZOG8fU10GKfD84QsoNGZgw4pc1aUQxR0GEqIAFeZn4BwDScKRUmLf0UvYcd1SCCFUl0MUdxhIiAJUaMzAuUEGkkRz4tIwztnH8KF1i1WXQhSXGEiIArTCmIHLE9NwjHHqbyJ58dglpKck4SZzgepSiOISAwlRgArzMwCA40gSzEvH+rDNXID0FIPqUojiEgMJUYCKjK5Awm6bxDE8MY32UzZ8aN0S1aUQxS0GEqIALcpOQ6ohiQNbE8hrJ/ox5ZS4g4GEKGwYSIgClJQksNyYzkCSQF46dgnmRVlYVZCpuhSiuMVAQrQAnGmTOKSUeOlYH7triMKMgYRoAQqNXIskURy7eBm9jnFO9yUKMwYSogVYwUCSMF461oeMFAO2lJhUl0IU1xhIiBagMD8DfZcnMD7lVF0KhdlLxy7h5lJO9yUKNwYSogW4fkUeAKCl86ziSiicRien0Xl6ELetZXcNUbgxkBAtwHUrcnF/+UrU//49XBwaV10OhcmBkzZMOSW2r+HqrEThxkBCtEB/d/d6pKUY8KXHO9E/PKG6HAqD17sHsCQnDaWLs1WXQhT3GEiIFigvMwX/+fly9NjGcO8jr7GlJA69dqIf29cs4tV9iSKAgYQoCDeuNOKZv9iOiWkn/udvj6ouh0JocGQSR3qHsH3NItWlECUEBhKiIK0wZuDrH1uPZw6dx+vd/arLoRB5wzoAKcHxI0QRwkBCFAL3bS5E2ep81D93THUpFCKvnuiHeVEWludlqC6FKCEwkBCFQFKSQO1tZhzqsePI+SHV5VAIvH6iHzezdYQoYhhIiELkjmuXYHFOGva2n1FdCgXpnH0MpwZGsb2U40eIIiU5kCcLISoB1AJoBWAFsANAu5Syxe05u9x2KZBS1vlx3Dn3CXY7USSkGJJQXVaEx988jd13r+fKnjHstRP9EALYVsoWEqJICbSFxAigEkCjduv2CCPNAOxSygYpZQOAbiFE/VwHnG+fYLcTRdLO8pUYGp9G29GLqkuhILx+oh/Xr8iDMTNVdSlECUNIKf1/shBVANqklHYv28wAugHk69uFEEYAg+6PBbIPAFMw2729ppcacgE4HA4HcnNz5z0HRPO553uvoHRxNr73mc2qS6EFkFJiy7f24T5LIXZ/bL3qcoii2tDQEPLy8gAgT0oZ1AC6UI4hMQOAewhw+7p8gfsEu/0qQog0IUSufgOQM9c3RRSoHdctxYvHLmFyekZ1KbQAJ/tH0Hd5AtvM7K4hiqSFBJKdQogqIUSNR9eIFZhtofBk9nGs+fYJdrs3uwE43G68OhqF1F3XLcPl8WnsPzmguhRagAMnbUgSQNnqfNWlECWUQANJF1xdNi1Syia4xms0A4CU0gqgDa4xJgBmB8H6NN8+wW73YQ+APLdb0TzPJwrI+uU5KDRm4A+HOY4kFh04acOGFXnISU9RXQpRQglolo0WANw9BaBRCGGUUtqllDuEEPVCCBMAG7QWDLd/vR1zzn2C3e7l9SYAzF4JjdeooFATQmDHdUvxh8MX8M17N/D/WIzZf9KGj16/THUZRAkn0Gm/Ve6zaqSUdu3N1gxX6wncp9y6daV0zHXc+fYJdjtRpN2+bjF+/PopdPeNYM0SXik2VpwdHMU5+xi2lJhUl0KUcPzustE+6Ju1mTHujwEfjOWweOxWCaBlrtku8+0T7HYiFbaWmJBqSMKr7/epLoUC0H7KBgCoKGYgIYo0vwOJ9gHf4NFtU4MrP/ybPcZw1AJwb70weyxiNu8+IdhOFHGZqckoW52PV97nxfZiyYGTNqxdmg1TFtcfIYq0gLpsAOzxsipqtdv9WgAWrRWlFECtR4DRV3ptCGCfYLcTKXHLNYvwby+ewJRzBikGXqUhFuw/acPNXJ2VSImAFkaLR1wYjcLlnbMOfOKRV/FU7TaOSYgBfZcnUPE/2/C9z2zGJzetUF0OUUyI1oXRiMjNhhW5MGam4LUT7LaJBfr4kS0cP0KkBAMJUZgkJQlUFJu4QFqMOHDShtUFmViWl666FKKExEBCFEZbS0x464wdE9NO1aXQPPaftLF1hEghBhKiMLrJXICJ6Rm8fdahuhSaw9D4FN67MIQKjvUhUoaBhCiM1i/PRU5aMvZb2W0Tzd46Y4eUXH+ESCUGEqIwMiQJlBfnY/9Jm+pSaA6dpwdhykpFcUGm6lKIEhYDCVGYbTUXoPP0IKacM6pLIR+6Tg/CssrI6w4RKcRAQhRmW0tMGJ104p1zHEcSjZwzEm+dGYRldb7qUogSGgMJUZhdX5iHzFQD9lvZbRONjl24jJFJJ8pWMZAQqcRAQhRmKYYklK3OxwGuRxKVOs8MIjlJ4IYio+pSiBIaAwlRBGwtMaHj1CCcM4l9qYZo9NbpQWxYkYuMVIPqUogSGgMJUQRsNRfg8sQ0jpwP6lIPFAadHD9CFBUYSIgi4IaiPKQlJ3EZ+SjTd3kCpwdGUcZAQqQcAwlRBKQlG7B5lREHuB5JVOk6MwgADCREUYCBhChCKopN6Dg9CCk5jiRadJ0exIq8dCzPy1BdClHCYyAhipCKYhNsI5Po7htWXQppOk9z/AhRtGAgIYoQy+p8JAmg/dSg6lIIwMS0E2+fc7C7hihKMJAQRUh2WjI2rMhDO8eRRIWjvZcxOT2DzVwQjSgqMJAQRVBFsQkHTjGQRINDPXakGpKwfnmO6lKICAwkRBFVUZyPs4Nj6HWMqS4l4R3ssWP9ilykJXNBNKJowEBCFEHlxSYA4PTfKHCox44bi/JUl0FEGgYSoghanJMG86IstLPbRinH6BSs/SO4cZVRdSlEpGEgIYqwimLXdW1InbfP2QEAm3hBPaKowUBCFGEVJSYcu3gZjtEp1aUkrEM9duSmJ6O4IEt1KUSkYSAhirAtxSZICXScZreNKgd7HNi00oikJKG6FCLSMJAQRdhKUwaW5KRx+q8iUkoc7LGzu4YoyjCQEEWYEAIVJSYukKZIr2Mc/cMT2LTSqLoUInLDQEKkwJZiE94558D4lFN1KQnnUI8dALCJU36JogoDCZECFcUmTDldXQcUWQfP2rEiLx1LctNVl0JEbhhIiBRYtywHOenJ7LZR4FCPnd01RFGIgYRIAUOSQPnqfA5sjTDnjMQ7Zx0MJERRiIGESJHyYhO6Tg9i2jmjupSE0d03jJFJJ2fYEEUhBhIiRbaUmDAy6cR7Fy6rLiVhHDxjhxDARg5oJYo6DCREitxQlIfU5CReaC+CDp61Y+2SHGSnJasuhYg8MJAQKZKWbMCNRUZeaC+CXANa2TpCFI0YSIgUqijJR/spG6SUqkuJe+NTru4xDmglik4MJEQKVRSb0D88iZP9I6pLiXuHzzvgnJEc0EoUpRhIiBSyrM6HEEDHqUHVpcS9gz0OpCUnYd2yHNWlEJEXDCRECuWmp2D9slyuRxIBh3rsuL4wDykGvu0RRSP+ZhIptqXExIGtEXDoLK/wSxTNGEiIFKsoNuH0wCguDY2rLiVuDY5M4vTAKGfYEEUxBhIixSqK8wGA3TZhdOisHQBwI2fYEEWtgFYHEkJUAqgF0ArACmAHgHYpZYvbc3a57VIgpazz47hz7uPH9hoARgB2AKUA9kgp7X58S0TKLclNx+qCTLSftOHjN6xQXU5cOtTjgDEzBatMmapLISIfAm0hMQKoBNCo3bo9wkgzALuUskFK2QCgWwhRP9cB59vHj+27ADylbW8CsAfAowF+X0RKVRSb0M6ZNmGjjx8RQqguhYh8WEiXTYmUUkgpS7UAAAAQQpgBVAF4yu25TwHYJYQwejvQfPv4ecwd7q0h2tdeX48oWm0pNuHohSEMjU+pLiXuSCm1FVqNqkshojmEcgyJGZgNBPD4unyB+/hzTLsQolUPKFqIsfoqUgiRJoTI1W8AuCgBKbelxAQpgU62koTc2cExDIxMYjMDCVFUW0gg2SmEqBJC1Hh0x1gBwEdriNnHsebbx59jPqx9PajVUymlrJ2j/t0AHG63s3M8lygiVhdkYlluOt6wDqguJe4c7LEDcF3MkIiiV6CBpAtAm5SyReuu6dbGeEBKaQXQBtcYEwCzg2B9mm8ff46ptZjUA2gBsAtAta8uIs0eAHlut6K5aiSKBCEEbi4twOvd/apLiTuHeuxYacpAQXaa6lKIaA4BzbLRAoK7pwA0CiGMUkq7lHKHEKJeCGECYMMHXSc+u1Dm22e+7VqrSKuUslrrrmkG0AnXbBtvrzcBYEK/z0FuFC22lRbglwfPwT46CWNmqupy4gYXRCOKDYFO+61yn1UjpbRrH+hmuFpP4D4l162lomOu4863j6/tWgAxSinbtOdZAZQJITo9ayWKdttKCyAl8KbVho9ev0x1OXFh2jmDd8458JENPJ9E0c7vLhstCDRrIcD9MeCD1gqLx26VAFrmWhNkvn3m2W6Ga+0RT40+vxGiKFWUn4lVpky8yXEkIXP84jDGp2Y4w4YoBvgdSLQA0ODRbVODKwNHs8cYj1oA7q0bZo9FzubdZ67tWsuIxcuYkTK2jlAs4jiS0Dp01g5DksCGFbmqSyGieQTUZQNgj5dVU6vd7tfCFRDMcI3hqPUIMPpKrw0B7DPf9moAu4UQA3C1lhhxZaAhihnbSgvw8/Ye9F2ewOIcDsIM1qEeO9YuzUFmaqBvdUQUaYEOarXjyjDhub0NrlkxvrY3AWjyeGy+febbbgcDCMWJbaUFAIA3rAP45CYuIx+sgz12bF5lVF0GEfmBF9cjiiJLctKxZkk23ujmOJJgjUxM4/jFy5xhQxQjGEiIoszNpQV4g+NIgvb2WQdmJGBZna+6FCLyAwMJUZS5ubQApwZGcd4+prqUmPZWzyBy0pKxZnG26lKIyA8MJERRZmtJAYQAXjvBVpJgvHXGdUG9pCQufkgUCxhIiKJMflYqNhbm4ZX3GUgWSkqJt87YcSPXHyGKGQwkRFHotmsW49UT/ZiZkapLiUlnB8fQPzzBGTZEMYSBhCgK3XrNIthGJvHueYfqUmKSfoVftpAQxQ4GEqIoZFmdj+y0ZLx8vE91KTHprTN2rC7I5BV+iWIIAwlRFEoxJGFbaQFePs5xJAvxVs8gNrN1hCimMJAQRanb1i5G15lBXB6fUl1KTJmYduLwuSF21xDFGAYSoih12zWLMD0juWprgI6cH8KkcwabV3FBNKJYwkBCFKVWF2RhdUEmXn6f40gC8dYZO1KTk7B+Oa/wSxRLGEiIotht1yzmOJIAHeyxY2NhHlKT+fZGFEv4G0sUxW5buxhnbKM41T+iupSYwQGtRLGJgYQoim0rLUByksAfOf3XLxeHxtFjG+MF9YhiEAMJURTLTkvGlhITXnjvkupSYsKBkzYAQEWxSXElRBQoBhKiKHfn+qV4o3sAwxPTqkuJeu2nbChZlIXFOVwQjSjWMJAQRbnK9Usw6ZzBq5xtM68DJ23YwtYRopjEQEIU5VYXZOGaJdloO8pum7k4Rqdw7OJlVJQwkBDFIgYSohhw5/qlePG9S3Dy6r8+dZy2QUpgKwMJUUxiICGKAZXrl2BgZBIHewZVlxK19p+0YVluOoryM1SXQkQLwEBCFAM2r8qHKSuV3TZzeOX9fty8pgBCCNWlENECMJAQxQBDksAd65Zg39GLqkuJSn2XJ3C0dwi3XrNIdSlEtEAMJEQxonL9Ehy/OIwzA6OqS4k6r3e7ltffvoaBhChWMZAQxYhb1y5GqiEJbWwlucrLx/tx7bIcLMlJV10KES0QAwlRjMhOS8bNawrw3OELqkuJKlJKvHqij901RDGOgYQohty9cTnaT9lwaWhcdSlR4/D5IVwcmsCt1yxWXQoRBYGBhCiG3HXdUhiEwO/fZSuJ7nfv9CIvIwXbSgtUl0JEQWAgIYohxsxUbF+zCL99p1d1KVFBSonfvdOLj2xYihQD386IYhl/g4lizD3stpl1pHcIpwZGcffG5apLIaIgMZAQxZi7NrDbRvfs267uGk73JYp9DCREMYbdNi4jE9P42YEz+NTmQnbXEMUB/hYTxSB22wBP7j+D4fFpPHybWXUpRBQCDCREMUjvtknUNUnGp5x49BUrPrW5EIVGXkyPKB4wkBDFIGNmKm65ZhF+ffC86lIiTkqJXS1vwzE2hS/fsUZ1OUQUIgwkRDHqU5sL0Xl6ECf7R1SXElH//sduPHPoPL6zcxNKFmWpLoeIQoSBhChGfWTDMuSkJePprrOqS4mYS0Pj+N6+91Fzmxkfv2GF6nKIKIQYSIhiVHqKAffcsBxPd53DzIxUXU5E/ODFE0g1JOEr7KohijsMJEQx7E/LinDOPob9J22qSwm78/YxPHngDGpvL0VeRorqcogoxBhIiGJY+ep8rC7IxC8SoNum/ZQNU06JB7auUl0KEYUBAwlRDBNC4L7NRfj9O70YnZxWXU5Y9TrGkZOWDGNmqupSiCgMGEiIYtx9lkKMTDrxXJwvJX/BMY5leemqyyCiMEkO5MlCiEoAtQBaAVgB7ADQLqVscXvOLrddCqSUdX4cd8595touhGgGsFerx+6+n5TSOt9rE8W6laZMbC0xobnjLO6zFKkuJ2zO28ewnIugEcWtQFtIjAAqATRqt26PMNIMwC6lbJBSNgDoFkLUz3XA+fbx45gWAM0AOgF0u93mfF2iePLZravwhnUAJy5dVl1K2PQ6xrGCLSREcWshXTYlUkohpSyVUjbpDwohzACqADzl9tynAOwSQhi9HWi+ffw8ZqNWz+wNQK2UsnoB3xtRTPro9cuwKDsVj71xWnUpYdPrGMfyPLaQEMWrUI4hMQOAlNKuP+D2dfkC9/HnmLMtNMBst1KHryKFEGlCiFz9BiDH13OJYkVasgGfrliFp7vOYXgi/ga3Tkw70T88geVsISGKWwsJJDuFEFVCiBqPrhMrAPhoDfF1Oc759pn3mO7jRLQWFbOUsmuO+ncDcLjd4n++JCWEz25dhdHJafwyDqcAX3RMAACWGxlIiOJVoIGkC0CblLJF667p1sZ46MGgDa4xJgBmWyt8mm+fBRyzzr0byYc9APLcbvE7CpASygpjBu66bhkee+M0pIyvlVt7HWMAwC4bojgWUCCRUlo9Zq48BaBKb8GQUu4AUKG1nlQB0JeP9DnbZb59/D2mEMLi5/cwIaUc0m8A4ncUICWcB7etxvuXhvGmNb5Wbu11jAMAu2yI4lig036r3GfVSCntQgjA1X3SpT3mPiXXqH3pc0yHP/v4ecxauGbXECWsbaUFWLMkG4+9cQrbSgtUlxMy5x1jyE1PRlZaQG9ZRBRD/G4h0YJAszZOw/0x4IOxHp6tFJUAWtwHpXo57pz7BHDMSnisQ0KUaIQQ+PzNxXj+8AWc6h9RXU7IXHCMYwXXICGKa34HEi0ANHh02dTgynDQ7DHGoxaAe+uG2WORs3n38WO7bnYQLFEiqy4rgikrDY0vx0+D4Xk7V2klineBtn/u8bJqqvt6H7UALForSilc64G4hwR9pdeGAPaZb7vOig/GlxAlrPQUA/781hJ85w/H8P/euTYuPsgvDI1hY6FRdRlEFEYi3kbjB0pbi8ThcDiQm5uruhyikLg8PoXt334B1eUr8Y8fv051OUG76Vv7sLO8CH991zrVpRCRm6GhIeTl5QFAnjZRZMF4cT2iOJSTnoKHbi7Gk/vPwDYyqbqcoA2OTvIqv0RxjoGEKE49tL0EAPDj10+pLSRIY5NOTEzPID8rRXUpRBRGDCREccqUlYrPbl2FH792EvbR2G0lsY+5amcLCVF8YyAhimNfur0UzhmJH7x4QnUpCzY4MgUAyGcgIYprDCREcWxxThpqby/FT14/jR7bqOpyFkRv3cnPZJcNUTxjICGKc39+awmMmSn43384prqUBRkcdbWQsMuGKL4xkBDFuczUZPz1jrX49cHzeOesQ3U5ARscnYQhSSA3ncvGE8UzBhKiBFBVVoS1S7Pxrd8djbkrAdtHJ2HMSIF23SwiilMMJEQJINmQhN13r8cb1gH85u1e1eUEZHB0CkaOHyGKewwkRAnijnVLcM8Ny/HfnzmMwRhaLG1wdJIzbIgSAAMJUQL5p09swPSMxP/47VHVpfjNzhYSooTAQEKUQBbnpOHv716PX3SdxSvv96kuxy92LhtPlBAYSIgSTHV5EW4uLcDup9/ByMS06nLmZR+d4hokRAmAgYQowQghsOe+jRgcmcTf/fKdqJ91wwvrESUGBhKiBLS6IAvfum8jfn3wPH52oEd1OT7NzEg4xqY4qJUoATCQECWoe28sxANbV+GffnMYh89H54JpQ+NTmJFcNp4oETCQECWwf/z4dbhmSTa+8kQXHGNTqsu5CpeNJ0ocDCRECSw9xYB/e8CCwdEp1DzWgfEpp+qSrjCoX1gviy0kRPGOgYQowa0uyMIPP1+Ogz12/NXeg3DORM8gV4feQpLBFhKieMdAQkQoLzbhkc9a8PzhC/jGM+9GzcybiekZAEB6Ct+qiOIdf8uJCACw47ql+NanNuLxN8/gn589ipkoaCnRgxEvrEcU/3g9byKa9ektqzDlnMH//8xh2EYm0FC1CanJ6v5u0TNREvMIUdxjICGiK3xuWzFMWWn4q70HYRudwr8/YEFWmpq3ihmthSSJLSREcY9dNkR0lXtuWI4ff6ECnads+OQjr+Jo75CSOhhIiBIHAwkReXXzmkX49V/cghRDEu79wWv46RunIj7YVX855hGi+MdAQkQ+rVmSjV99ZTs+XbES//jrw3jwRwfw/sXLEXt9tpAQJQ4GEiKaU3qKAd+893r86KFynLGN4qP/+gr+6ZnDGByZDPtrz7CFhChhcFArEfnlw9cuxfY1i/Bfr53C9/e9j73tPfiUpRBfuLkY1yzNCctrsoWEKHEwkBCR39KSDfjS7aWoKivCE2+eweP7T+PJ/WewpdiEuzYsReX6pShelLXg4ztnJI72DuHASRvaT9nw2ol+pCYncdovUQIQ0bIioypCiFwADofDgdzcXNXlEMWUyekZ/O6dXjxz6DxePdGPyekZrDJl4vrCXFy3PBdrluRgUXYqTFmpyM1IwYyUcM5ITEzNoH94AheHJtDrGMP7F4dx7OJlHL94GaOTTqQakrBpZR62r1mEj2xYhvXL+btJFI2GhoaQl5cHAHlSyqCm4zGQMJAQhcTo5DRePt6PAydtONo7hCO9Q35dQTgtOQlrlmRj3bIcXLssB5tX5WNjYR7SUwwRqJqIgsFAEkIMJEThIaWEbWQStpFJDIxM4vL4NAxJgCEpCSkGgcXZaViSk47cjGQuDU8Uo0IZSDiGhIjCQgiBguw0FGSn4RrVxRBR1OO0XyIiIlKOgYSIiIiUYyAhIiIi5RhIiIiISDkGEiIiIlKOgYSIiIiUYyAhIiIi5RhIiIiISDkGEiIiIlKOgYSIiIiU49LxmqGhoJbgJyIiSjih/OxkIAFMALBy5UrVdRAREcUqEwBeXC9INu3fIgCXVRaSQHIAnAXPeSTxnEcez3nk8ZxHnn7ObfM9cT4MJB+4HOylk8k/bpea5zmPEJ7zyOM5jzye88hzO+dB46BWIiIiUo6BhIiIiJRjIAEmAPx37V+KDJ7zyOM5jzye88jjOY+8kJ1zIaUMvhwiIiKiILCFhIiIiJRjICEiIiLlOO2XKIEIIVqllDtU10FEsUcIUQmgVkpZ7WVbjdtdo5SyIdDjJ3QgCcUJpMAIIXZpX1YAsEop61TWk0iEEFUAKlXXkSiEEPUAurW7Nilli8p64p3b+7kRQAGAPVJKu7KC4ogWRHbAdW7NXrbXwO0zVAhRJYSoD/T9PWEHtXo7gQAq+AEZPp7/QYUQzQDgLW1TaAkhjABqANRLKUO3khF5JYRohesvSasQwgKgk+c9fLQ/dFqklFbtvhGu/+u1SguLM9rn5G4pZZnH490AdujnX3tsUEqZH8jxE3kMSR2A2b9YtL9eanw/nYKhvUFUav/q9gCoEkJclbgp5HYCaFJdRCLQ/tjp0t+cpZRdAMrm3ouCdMWHodYywveVCNDe083u519j1MK43xIykITyBFJAzLjyTcLq9jiFifZ/ukN1HQmkHkCr+wNaKKHwMbl1B1Nk+Xr/ts+xzatEHUMy3wnkm0eIaX+xeDbf6T8Hz2BIoVUupWzyaJ2iMNDOsRGuP270FtdSdgWHXR2AViHEDgC1bjcKP5OPx21zbPMqIVtIEMITSEGpBdDmpaWKQkQIUSWlZFdN5Ogh2ySlbNLOfas+XorCQ0rZBtegy0q4BhK3830l9iRqICHFtG6ESgAc0Bom2tgcu+o6Eoz+B81sF5n2YcmxUmGknVsLXK2wTQCa2YUTMTYfj5vm2OZVonbZhOwE0oLVAyjjtLywqgRQqjVjA66uBH06ajunoYaF1eNfdxYfj1Pw6t1m69Vqs5yahRAtbCkJu9mZTR7v50YE+P89UQNJyE4gBU4I0QjXlEi76lrimWdXjdYqVcPxDOGjTfMFOBYtYrT/11e8b0spW4QQDXCFcnZZhpGU0i6EsML1B73dY1tAvwMJ2WWjfRDqJ9BzG99Ewkgb6Ffvtl6AhTObKM50wfvAeb63RFY3+AdmqPkaY1kPt0HE2vt8wH/4JGQg0YTkBJL/tEV1jADMQohK7X4t+KYRdnoQ1L5u1lZepPCog2uAJYDZc8+ugzDR/oi0eBmjU6aN36EgaX841sP1f9sihGh0X+lca40dEELUaGN3Shey8nnCrtQKzK7uZ4e21DCbssNHmw456G0bV7CkeKO9WZfq9/neEl7a+8tu7e4AuHR8TEroQEJERETRIZG7bIiIiChKMJAQERGRcgwkREREpBwDCRERESnHQEJERETKMZAQERGRcgwkREREpBwDCRHFJSGEUQjRqa1Ma5zjeTXac3h1WCKFuDAaEflNW567G0AbPrg2yy64lv9vgWvV43IAZillvrZPvXa/+qoDhrdWI4Dd/q6SKoRolFLWzv9MIgqHRL3aLxEtjBFAnft1KrTr4rS5f/ALIZrd9mmHazlvIiKfGEiIKBBmuFpC5tOqfyGl9Of5RJTgOIaEiALi51VrrXON2yAi8sQWEiLym7+tHfpl34UQFgCPao+VaY9VAqjXnvowXK0uJrguF1+rXSnXBuB+uK7Y2uV+bG1MSru2n1279LlftIGreqAKeH8iCh8GEiIKGylllxCiDkCj22Ntbo+Z9JAjhOgWQtTrY1GEEIArzJTp+wohWgHUuwWeZiGEVb8/FyFEFVwBRH89M4CqEH2rRBQkdtkQUbjZfDxm9ggSnl1BVrhaMQDMtrZUeuzTCsDfmTEmANV6V5LW9TRvkCGiyGALCRGpYvfyWPcc28vhGpvi3qphhKv7Zl5SyiYhRDWAQSFEF4C97rOFiEgtBhIiiinBzNqRUu7QumoqAdQJIQr8XaeEiMKLXTZEFCs64NaFo9MCxry0FVmNUkqrNpC1DK5gQkRRgIGEiIJl1G7hOO4sbbZNizYLB8DsaqyWAI5X4/GYP1OYiSgC2GVDRAuiLwmv3XZqs2I63afRagNR6wGY9aXZ3R4zCiEaAdQB2A1Xa4VZCGGDKyhc8RwppV1KWS2EqNem79oB19gQP0u2azXpY1DMcE07JqIowGvZEFFc4rVsiGILu2yIiIhIOQYSIiIiUo6BhIiIiJRjICGieFapLS9v9PUEbTpwM65clI2IIoyDWomIiEg5tpAQERGRcgwkREREpBwDCRERESnHQEJERETKMZAQERGRcgwkREREpBwDCRERESnHQEJERETK/V/r3iULsEdxPAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 600x400 with 1 Axes>,\n",
+       " <AxesSubplot:title={'center':'BusFreq 4 Output f'}, xlabel='Time [s]'>)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.plt.plot(ss.PVD1.fHz,\n",
+    "                a=(0,1),\n",
+    "                hline1=59.9,\n",
+    "                hline2=59.8,\n",
+    "                title='PVD1 Read f')\n",
+    "\n",
+    "ss.TDS.plt.plot(ss.DGPRCTExt.fHz,\n",
+    "                hline1=59.9,\n",
+    "                hline2=59.8,\n",
+    "                title='BusFreq 4 Output f')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PVD1_2 power command\n",
+    "\n",
+    "`PVD1_2` power commands are locked to 0 **immediately**.\n",
+    "\n",
+    "Once the protection was released, they returned to normal **immediately**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 600x400 with 1 Axes>,\n",
+       " <AxesSubplot:title={'center':'PVD1 Q\\\\_tot (reactive power command)'}, xlabel='Time [s]'>)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.plt.plot(ss.PVD1.Psum,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 P\\_tot (active power command)')\n",
+    "\n",
+    "ss.TDS.plt.plot(ss.PVD1.Qsum,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 Q\\_tot (reactive power command)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PVD1_2 current command\n",
+    "\n",
+    "Consequently, `PVD1_2` current commands are locked to 0 **immediately**.\n",
+    "\n",
+    "Once the protection was released, they returned to normal **immediately**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 600x400 with 1 Axes>,\n",
+       " <AxesSubplot:title={'center':'PVD1 Iq\\\\_ul (current command before hard limit)'}, xlabel='Time [s]'>)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.plt.plot(ss.PVD1.Ipul,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 Ip\\_ul (current command before hard limit)')\n",
+    "\n",
+    "ss.TDS.plt.plot(ss.PVD1.Iqul,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 Iq\\_ul (current command before hard limit)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PVD1_2 output current\n",
+    "\n",
+    "As a result, `PVD1_2` output current decreased to 0 **gradually**.\n",
+    "\n",
+    "When the protection was released, they returned to normal **gradually**.\n",
+    "\n",
+    "Here, the `PVD1` output current `Lag` time constant (`tip` and `tiq`) are modified to 0.5, which is only for observation.\n",
+    "\n",
+    "Usually, power electronic device can response in ms level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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21nLNJS1s27yeay+Jcd2GFi5qXqE1Ixbj+GOw7rqi7Kol1sr1N70UbnopAHfMpLjnVxOc+N7LmXj0u1x+ZIR1hz8LY7AjuYvxi5/NS1bex1X146y89Foubn8qbWvXFSUWETkP1tnI0T0zAgwaY/rmmAa7G/hw4P4q4JGQwpMyc+T4FHsP/JJ/u/1BnjgxxaVtjVxxwUouX91Ex6Ux2prqWd1Ux8a1TbSvXUlbU13UIZeP130JQhpfUVdTRcelrXDpu4B3ATA5fpiHf/pDnjl1GWOPJ1nxwLfZPPVf1NybAuAYq/jvVa/i/vhrubrpFE9J/YzmdVew9tIn0RJbrYRSpACFJBvjc2xvm6dsoToF79Nam/D+yHN23Vhrp4Ap/75/Qrj3Q7/FyhXuyz1RtYq/b/szAN46vpvmlJOxj0839/BQXTvPOfk/3HD6fzPK7qrfzJdWdbNm5nF6nA9nlKWo5oNrPgDAG479HRclf5VR/oVVr+Le+l9ny+nvsPXklzLKflF7NXtafo+G1EneMf7+Wa/7Q23vY6qqgVdOfJT49M8yym5reimjDTdwzZkxXnLi1oyyX9Vs4OOxtwLw7idm9z59pPWPGa9ey4uP38q1U5mH8/aGm/l20/PYePZnvGryoxllTlUb/9zm7u+Pjr6fRnsyo/wTLW/m0drL6TzxRZ525vaMsrEVT+d/Vr6cC2ce5fedv88oO0sdf73mLwDYcezDrE0+nlH+uVWv4f76a3jGqf/lN0/9T0bZfXXX8p/Nr2ZVcoK3HfvArNfav/ovSZpaXu0MculM5hCgLze9jH0z1/PgEyepqjK8ouMSdjwrTnztyln7kZA0l7YlobntAq555ou5Bng9AB9neuoUDyfu4YkHf8KZx+5jMnkFYw8d4+yxr/Na87fpupO2iR/XdfBPq/+U+toq3nH0/Rgyx799ds3bcGrW8tyJ/+Sq03dklB1YeRM/Wnkz66cO8qJjn8woO1HdwqfWvhOA1x3+KxpSJzLKv9D2Bg7VXcYzjn+N605+DwA/77m3YTPfbnkxq6cP0X30XzLqTZtaPnqhe97rfuKfaZs5nFF+W2wbD6x4MtedvJ3fOD6SUfZA/dXc1vpKGpPHec2RD806lh+94N3MVNXx4vGPc8nZB9LbrYVvN7+Iexu3cNXpH/Pcic9n1Hus7lI+3/ZGjE3y5sffN2u//772nUxWtfK8iT086czdGWU/bLqZ0ZXP4fKpn/Ii59MZZU71aj615o8AeOPh/8uK1OmM8j1tb+Lx2vXcdPxLXHfq+xlldzQ+g2+tehEXTj/CK8f/OaNsyjTwL2vdY/i6o39Da/KJjPIvtLyGB+qv5uknv8ENJzKP4X0rnspXml9Jc/IYO47+1blj5P388Nq/wJpqXnXsI1wy/VBG3a+uegX3rNjEdad/yPNOfCGj7KHaK9gT+31q7VneceTPyfZPbe/mRHUzL5/4JE86+5OMsm82/TY/aHw2V03dzcsnP5VRdqTmIv619R1Mn848ty9WIclGAnIurBXj3NTUQuvMW+4NMH0Ad7Bo+rEFxJw2VddKbZ07IG+6ehWrvT70mZMxppKZiwWtbGpgdX09tamVTCUze4KqGty6zdMNTJ3KLEtRld5v6nQrU9NnMsobGleyuqGeFaxkajqrh2lFjNUr61mRnGHqxOzep7aV9ZytqoepFqZMZnl9YxOrm+ppqGpi6mxWTHWxdExTk7P329LUgKmpp3q6mSmbWV7b6L7WlWcamTqTWTZT3Zre79kTbVSnMsckrGpqZHVdPdXJZqZSmXWrVzTPeQynTV16v8lTMaZmpjPKGxubWN1QT71dydRM1nvj7bcxuYKpk7Nf6+qV9SRNLakzsVnHqbFxFc+6cA2/98zLedG1F2vwZqkdewj2/Tl0vg/aNkYWRm19I5f+2tO49NeeBsANwNsAm7oR58gbOfLIzzl+6CBnn3iIU1NJ1rc0MDWdpMrOYEhl7GsmmWLapLCpGaps5u9xKjnDdDJFMpWk2p7NKDOpaaaT7r6q7PSs8mQyyXQyhU3OUJOaykhxbHKaqZkUZ2eSVCWnMupVG8vUjLtfkzxLdTLz/DQzM8PUTIrUzMysMpM8y9RMippkkpqsMoCzyRTTqRQkp2c9r025rzWVTM46DiY1QzJlMdZSZWevbmtTFmssxqYwNpVVmgLmm+BgvH8N1mQtCOctm4+pImWqs4qqqKkyVFcZUibzPJCqqqW2ugpjwFbVkrKZLZs1NTWsqKmmurqWZPWKjLKq6joaaqtpqKpmprphVrRNddVYUw01K5ixmevH1NfVsWpFDfXJFczUZH0Bqm2keUUtNSnL2drZkyBWNtZRXV0LZ1Zxllhm1RWNtDbW0WBynONrW2lrquesyXzPFqsYU19t9mySQurMV+4lFnuttVsDZR3AgfmeM+v5NRtFFjb+ADS0QkMs6kgqU+Jb8MmXwB/+GNrmGzsuIqVUrNkoha7/2w+kB2Z6C3L1Be7Hs1YDXbDOfOVea8c+Mu3Kqi+ydP9yA9zxmaijqFzHD7k/V2lQpkg5KmiAqLV2yBiz01/1E1idNQ21EzcRGMq3Th7lA8aYnd7ddmCftTa9f5GiqKqBHM24UiLHfwUrYlA7u3lZRM5/Bc9Gme9aKF4SMCsRWOj6KUstF1kyUwWpmYUfJ+E4fkitGiJlbNlPfRUpiapqSKllIzJXPr9oa2yIyPKjZEMEwFTDrNHuUjLtz406AhEJkZINEYB33Q9VhY6XlqL5yRfgomthdXvUkYhICHR2FQElGlH7fC/8/LaooxCRkOgMKwKw57Xwo1uijqJyJaehWgupiZQrJRsiAIfuBufhqKOoXDbpjpsRkbKkZEMEvHU2NEA0Ev4qxiFdhE1EoqdkQwTcb9VaZyMaNgXrfh0a10QdiYiERLNRREDrbESpqhp6vxV1FCISIiUbIgDPfQ80rY06ChGRsqRuFBGAq14A6zdFHUVlmjoB718N9/xn1JGISEiUbIgA/PSLkPhm1FFUJpt0x8sYE3UkIhISJRsiAN//J7jzP6KOojL5Y2U09VWkbCnZEAFvNooGiEbCn3Ksqa8iZUvJhgh4s1E09TUSatkQKXuajSICbrJh1bIRicbV8JYfQsslUUciIiFRsiECcHEH1NRHHUVlqq6BC66OOgoRCZGSDRGAm98TdQSV6+RR2PceeObbYe1VUUcjIiHQmA0RgOnTcPZk1FFUpqkJuOPTcOLxqCMRkZAo2RAB+M8euPXVUUdRmVLebBQNEBUpW0o2REDXRomSPzBXU19FypaSDRHQJeajpKmvImVPyYYI6BLzUWpcDTe+E5rXRR2JiIREs1FEQN0oUVp1IXS+N+ooRCRESjZEAF7wQXWjROXMBPzqDrhkE9SvjDoaEQmBulFEAFY0Q0Ms6igq05GfwSdfAs5DUUciIiFRsiECMPpv8JWdUUdRmawGiIqUOyUbIgCH74MHvxN1FJVJV30VKXtKNkRAA0SjlJ76qtORSLnSX7cI6KqvUaqug+b17k8RKUuajSICWmcjSpc+Hd75k6ijEJEQKdkQAbjyBbC6PeooRETKkrpRRAAuewZ0vDbqKCrTwW/AX18FJ45EHYmIhETJhgjAkfvhvi9HHUVlmj4NJw5FHYWIhEjJhgjAT78I//2HUUdRmTT1VaTsKdkQAc1GiZKmvoqUPf11i4B7ifmUro0SCT/JU8uGSNlSsiECmvoapY03weu/ArWNUUciIiHR1FcRgJUXwIVPiTqKytS0xr2JSNlSy4YIwLVd8MZ9UUdRmR4dg31/ruXiRcqYkg0Ridbj98B3/w4wUUciIiEpuBvFGLMTcLy7MWvtwFLrFLJPY8w+a+3WAsMWmd+PPw1ffDu85wgYfeiVVHrqq777iJSrgv66vaQAa+2QtXYIGDPGDC6lTiH7NMZ0AZ2FxCySt9S0mvKjkEq6A3RFpGwV+lViFzDk37HWjgA9S6yT1z6NMTGgrcB4RfLjT7vUWhulZ1Oa9ipS5vJONowxcdwuDidHWc7WhoXqFLjPbcCefOMVKUiV16Oolo3SW3MlXP+aqKMQkRAVMmYjPsd2B4gtsk5e+/QSj5F5ozv32HqgPrBpVT71pML5q1dqrY3Si9/k3kSkbBVjRNY4hXdvLFQnuzxmrU3kue9dwETg9kiBsUkluuJmeMsPoa4p6kgqz8kn3AvhiUjZKkaysZhxFAvVSZcbY3qstcMF7Hs30BK4rS88PKk4K1rggqs1diAKP/53+Ojzoo5CREJUSLIxV8tCbJ6yherMW26M6QBG8wvPZa2dstZO+jfgeCH1pUIduge+8Adw2ok6ksqjAaIiZS/vZMPrxnC8QZ3ZZTnHUyxUJ499tgGdxpid3hTZfnCny3rTYEWK4/hjcMen4OzJqCOpPKmUpr6KlLlCu1F2E1jnwvvAHwrcj/vrZuRbZ75yLyEZ8G/AoLd9oMCuFZH5+QNENfW19GxSLRsiZa6gZMP7wI8ZY7q8pGCLtbY38JBOoLeQOnnsE0gnIbu8//fPNd1WZFH8DztNfS09Uw21DVFHISIhMtbaqGMIlTGmGZiYmJigubk56nBkuXrwdvj4C+FtY7C6PepoRESWhcnJSVpaWgBavHGQi6KLEYgAtGyAG9/pzkoREZGiUrIhAtB6GXS+F5rWRB1J5fneP8JnXxV1FCISIiUbIgBTx+GBb7s/pbQmfwVHfxF1FCISIiUbIgBHD8InXuz+lNKyuuqrSLlTsiECmo0SpZSmvoqUOyUbInDum7XW2Sg9mzy3zomIlKVCrvoqUr50ifnobHo9PPmlUUchIiFSsiECUFMHzZecSzqkdNZdF3UEIhIynVlFAFovh3feG3UUlenn++DMBFyryx2JlCt1lIpItO4ehv3/GnUUIhIiJRsiAMcfhw9dDYlvRR1J5dHUV5Gyp2RDBMAY9zLz06ejjqTypJJQpVORSDnTX7gInPtmnZqJNo5KpJYNkbKnAaIicO6btdbZKL2LO2DmTNRRiEiIlGyIgNbZiNKNfxR1BCISMiUbIgC1jfD6L8Oaq6KOpPKcPgYYaIhFHYmIhETJhgi41+a4/Maoo6hMn9sBNfXwyk9HHYmIhEQDREUAUinY91549EDUkVQeXRtFpOzpL1wE3Kmv3/1bOHRP1JFUHl31VaTsKdkQATfZMFWajRIFm1LLhkiZ01+4iM9UazZKFGxK62yIlDkNEBXxVSnZiMSrPxd1BCISMiUbIr7rXw1rNfW15Goboo5AREKmZEPE98IPRR1BZfryH0NbHJ7xlqgjEZGQKNkQ8R35GTS0wsq1UUdSWR75ka5JI1LmNEBUxPex34Kxj0cdReWxKU19FSlzSjZEfFU17uJeUlopzUYRKXdKNkR8VdVqzo+C1aJeIuVOYzZEfKZai3pF4aad0HxJ1FGISIiUbIj4ahu0kmUUrnlF1BGISMiUbIj43vqjqCOoTD/5ArReDhf/esSBiEhY9DVORKJ125/BT78YdRQiEiIlGyK+va+H73w46igqj6a+ipQ9daOI+I7+AhpXRx1F5UklNfVVpMypZUPEZzT1NRI2CVU6FYmUM/2Fi/h01ddoXNwBzeujjkJEQqRuFBGfqXbHD0hp/e6eqCMQkZAp2RDx3fweqFsZdRSVJ5V01zcxJupIRCQk6kYR8W18NlzSEXUUlWf3BvjBv0QdhYiESMmGiO/e/4L7vhJ1FJVH10YRKXvqRhHxHfgE1DXB1b8ddSSVxe9GEZGypb9wEV9VjQaIRkEtGyJlr+CWDWPMTsDx7sastQNLrTNfuTEmBmzz7rYDcWCHtdZBpJh0ifnSs9ZN8NSyIVLWCko2vKQAa+2Qd7/TGDNore1dbJ089tkP9FtrE175ILAX2FpI7CILMlWQnI46isrz9rugoTXqKEQkRMZam/+DjTkGbAy2KhhjrLV2zjlrC9XJo3wfsM9v7fCSk13W2rzOTsaYZmBiYmKC5ubmvF+rVKDb/wZmpuA5fxJ1JCIiy8Lk5CQtLS0ALdbaycXuJ++WDWNMHLeLw8lR1mmtHSm0DpBYaJ/W2uwWjC3ArOcK1KsH6gObVs31WJEMN74j6ggqz8xZ+MKb4Gk9cOlvRB2NiISkkI7S+BzbHSC2yDoF7dMY0+Vt3zFHPYBdwETg9sg8jxU558wknBqPOorKkpqGez4HE/ozFSlnxRiVNQ60FblORrkxJmaM6cFNTvYuMDh0N9ASuOmiC5Kfr+6Ez74q6igqi38tGg0QFSlrxVhno9BEI586GeVecuEPIO3JNc4j8NgpYMq/b7QEsuTLVLvTMKV0/OOtqa8iZa2QrxOJObbH5ilbqM685V6LRr83/dU34pV3zh2qyCLoqq+ll/LWNVHLhkhZy/sv3Jt66niDPrPLcg7YXKhOHvuMAzvJbOmIeT+dfGMXyYvW2Si92hXwm38Ka6+OOhIRCVGhXyd2E2hR8AZsDgXux/11M/KtM1+5tXYMGPDX2PBsB8bmSnBEFk2XmC+9uia4aSeseVLUkYhIiApaZwPS61z4H/5brLV9gbIeoM9a255vnTz2GQN6Ag9v957DyTNerbMh+Zk+4yYbdY1RR1I5pk/DA9+GSzZD0+qooxGRLMVaZ6PgZON8o2RDZBlzHoa/vRZe83lof27U0YhIlmIlGxqVJeI78An4/JuijqKypKe+ajaKSDlTsiHiO/YAPPS9qKOoLP4YGU19FSlrSjZEfBogWnpq2RCpCEo2RHxVNZr6WmpV1dDWrkG5ImWuGCuIipQHLepVeqvb4Q/Hoo5CREKmZEPE96St0HxJ1FGIiJQddaOI+C6+Hq7/3aijqCyP3QUfvAwe/0nUkYhIiJRsiPiO3A937Yk6isqSnIYzDpT5ej8ilU7Jhojv4Dfgi2+POorKoqu+ilQEJRsiPqMBoiWnqa8iFUHJhojPmHPftKU01LIhUhGUbIj4NPW19NZdB2/8hmYBiZQ5TX0V8a1aBxue7g5WNCbqaCpD/SpYvynqKEQkZGrZEPFd9Vvw+19TolFKh++Dr+yE007UkYhIiJRsiPisdbtRNA2zdJyH4EeDMH066khEJERKNkR8dw/D+9tg5kzUkVSOlAaIilQCJRsiPr/7RINES8e/yq6mvoqUNSUbIj7/27Wmv5ZOeuqrTkUi5Ux/4SI+/9u1WjZKp3UjPK0XalZEHYmIhEhTX0V86ZaNVLRxVJJ1T4V1A1FHISIhU8uGiC/+HHj7ndDQGnUklePkUXj0AKSU4ImUMyUbIr66Jmi9XDMjSunnX4NbngupmagjEZEQKdkQ8T3+Exh+A5x8IupIKoemvopUBCUbIr6TR+Cez8HZE1FHUjnSU191KhIpZ/oLF/FpNkrp2aSbaGiJeJGypmRDxKfZKKVXVQsNbVFHISIh09RXEZ9aNkqv4zXuTUTKmlo2RHwt6+E574bG1VFHIiJSVpRsiPhaLoHn9MHKtVFHUjnu+AwMPjvqKEQkZEo2RHxnJuFnt8FpJ+pIKsfpY3A0EXUUIhIyJRsiPuch+Ew3HD0YdSQiImVFyYaIz+iqryIiYVCyIeKr0mwUEZEwaOqriE8tG6V31W/D2quijkJEQqZkQ8RXUw9t7VBdH3UklaNto3sTkbKmbhQRX2wD/OEYbNgSdSSV45f74bt/F3UUIhIyJRsiEp1f/hC+/ddRRyEiIVOyIeI7cRg+eBn84utRRyIiUlaUbIgEnXEgeTbqKEREyoqSDRFf+kJsM9HGISJSZpRsiPiqvD8HrbNROmueBL/24qijEJGQFTz11RizE3C8uzFr7cBS6+RZDtAOYK3tLTRukQVpnY3Su/L57k1EylpByYb/oW+tHfLudxpjBuf78F+oTh7l/dbavsD+Bo0x+6y1WwuJXWRBdU3wxm9o3YdSOjPhXgAvtiHqSEQkRMZam/+DjTkGbLTWOoFt1lprFltnvnJjTAzYC3T75caYDuAA0G6tXfBykcaYZmBiYmKC5ubmvF+riJTA9/4BvvVXsOvhqCMRkRwmJydpaWkBaLHWTi52P3mP2TDGxHG7OJwcZZ2LqZPnPjcD8UCRn2DE8o1dJC+pFHy1Dx4ZjToSEZGyUkg3SnyO7Q5zf/AvVGfeci8Jac0q85OQnK0axph6ILje9Ko5nkNkth9+BC68BtZvjjoSEZGyUYzZKONAW5HrzFe+C+jN1RoSKJ8I3B4pMDapVP5sFA0QFREpqmIkG4UmGvnUyVlujOkHbvUHk85hN9ASuK1fRHxSqUy1pr6KiBRZId0ocw3GjM1TtlCdvPdpjOkCDi6QaGCtnQKmAvXme7hIpqpqsKmoo6gcv/EH8PQ3Rx2FiIQs75YNb+aH4w3qzC4bWUydfPfpDxYNTI+N5aojsmRbdsAFT446ispRVQXVBS/3IyLnmUK7UXZzboCm39owFLgfDyzAlVedPPbZAXQAY97+40AP7rgOkeJ6wQfg8mdGHUXluHsYPvWKqKMQkZAVtM4GpBfh8rs4tmQtuNUD9Flr2/OtM1+5t87GA+SY7TLf2h5Z+9Y6G5K/x+6ElRfCqouijqQyfPfv4Dsfgj/ROhsiy1Gx1tkoONk43yjZkIJ88DK48Y/gxndEHUllULIhsqyVfFEvkYpQpdkoIiLFpmRDJMhoNoqISLFpGLhIUFWNWjZK6YqtsGpd1FGISMiUbIgENcSgujbqKCrHhU92byJS1pRsiAS95ftRR1BZHrsLDt0N1/9u1JGISIg0ZkNEonPwG/C1d0cdhYiETMmGSNCnuuD2v4k6ChGRsqJkQyTIeRhOHI46ChGRsqJkQyRI62yIiBSdkg2RIFMNVslGybSsh8t0LRqRcqfZKCJBVVVq2Sila7vcm4iUNSUbIkEv6IcVuoZOySSn3VtdY9SRiEiI1I0iEnTZM+DCp0QdReX4/j/C32hRL5Fyp2RDJOiOz8J9X446ChGRsqJkQyTox/8O9/5X1FGIiJQVJRsiQUYDREVEik3JhkhQlaa+iogUm2ajiAQZLepVUlt2wHX/J+ooRCRkSjZEgi6/Earroo6ictSvdG8iUtaUbIgEPeudUUdQWe79b7j/K/Cyj0QdiYiESGM2RIJOPqELsZXS0V/Az74WdRQiEjIlGyJBX3oHfP5NUUchIlJWlGyIBGk2iohI0SnZEAnSbBQRkaLTAFGRoKpqsKmoo6gclz8L6pqijkJEQqZkQyTIKNkoqQ1b3JuIlDUlGyJBL/kHMCbqKCrHEz+H8QRc+fyoIxGREGnMhkhQdY3blSKl8dP/hi+8OeooRCRkSjZEgr73D/BffxB1FCIiZUXJhkjQsQfhV3dGHYWISFlRsiESZLTOhohIsSnZEAmq0jobJdW4BtZeHXUUIhIyzUYRCTJVmvpaSpte595EpKwp2RAJuuYVcOlvRB2FiEhZUTeKSNAlHfBrL446isrx3b+Dv7k26ihEJGRKNkSCHh2DO/8j6igqR3IaZk5HHYWIhEzJhkjQz74GI++LOgoRkbKiZEMkSLNRRESKTsmGSJAxmo0iIlJkmo0iEqRFvUqr47Vw9QujjkJEQqZkQySo9TK49Iaoo6gcKy9wbyJS1tSNIhJ0zSvgVZ+JOorK8bPb4LY/izoKEQlZwS0bxpidgOPdjVlrB5ZaJ4/yGLAN6LbWbi00ZpG8JWdg5gzUr4w6kspw6E6481Z43l9GHYmIhKiglg0vKcBaO2StHQLGjDGDS6mTR3kHbqIRA9oKiVekYAc+BgMbo45CRKSsFNqNsgsY8u9Ya0eAniXWmbfcWjvmJSGJAmMVKZyp0tRXEZEiyzvZMMbEcbs4nBxlnYups5h95hFnvTGm2b8BqxazH6lQpkqzUUREiqyQMRvxObY7uF0ci6mzmH0uZBfw3kXWlUpXVe3+TKWgSuOnQ7d+i3usRaSsFeNsOk7hYykWqrOYffp2Ay2B2/pF7kcqkfGSDbVulEb8OfCcvqijEJGQFWOdjcUkBQvVWfRAUGvtFDDl3zfGLHZXUome8jK4ohOqtARNSTi/hBOPw/rNUUciIiEqpGVjrgGasXnKFqqzmH2KhKeuEVZd6C5bLuG78z/gP/5P1FGISMjy/vpmrU0YYxxjTNxam8gqG1lsnUL3KRKqh38I3/t7ePktbuIhUiLWWpLJJDMzM1GHIhWgtraW6urqkj1foW3Fu4FOvKmqxpguAtNWvdklXVmLcs1bJ49yn9bYkPAdfwzu+xIkzwJKNiR81locx+HIkSMkkxorJKUTi8W46KKLSjLcoKBkw1o7YIzZ6SUEAFustb2Bh3QCvcBAvnUWKvcTGGA70GGM6Qf2W2uHC4ldJC/+bBRd+VVK5NChQziOQ3NzM83NzdTU1GismYTKWsupU6c4fPgwAOvWrQv9OQseBZfVajGcVTZEjlaJ+erksc8EbvKy4LLoIkvmz0bRwl6lUdcETWujjiIyyWSSiYkJ1q5dy5o1a6IORypIQ0MDAIcPH+aCCy4IvUtFCwmIBFVp6mtJPeMt8ObvRh1FZKanp7HW0tTUFHUoUoEaG92u4unp6dCfS8mGSNCaK2Hr+91v3CIlom4TiUIpf++UbIgEtW2EZ74d6rXKfUn84CMw9JtRRyEiIdPKRSJBJ5+Ah74L7TfrMvOlMDUJk7+KOgqpYIlEgsHBQQYGBojH4/T2uvMTDh48yPj4ONu3b6ery52/MDw8zODgICMjI3R2dtLf309HR0fGvrq7u3Ech97eXrq6unLu++jRowC0t7fT07PQtUzLg7HWRh1DqLyLsU1MTEzQ3NwcdTiy3D14O3z8hfC2MVjdHnU05e9bA7D/o/DH90cdSSTOnDnDAw88wMaNG1mxYkXU4VS0rVu3Eo/HGRwczNje3t5Ob28vO3fuBMBxHFpbW9m7d286CQkaHh4mFovR2XnuWqJz7bu3t5fx8XH27t2bsd1xHPbs2cPevXvZt29fQa+jkLr5/P5NTk7S0tIC0GKtnSwomAB1o4gEaTaKiAT09vbS13fu+j2xWCzdYpFLIpHISDTmMzg4iOM4DA2dm8Q5NjbGnj17cByH8fHxgmJdSt2wKdkQCdJsFBEJiMVigNti4Ovt7WVkZCRjW/bj89Xd3Z2RzHR0dNDT00M8PtdF0ee2lLphU7IhEqSWjdK6thu6/i3qKCQkIyMjDA8PZ3xzdxyHTZs2RRhVYQ4cOEBHR0dGEtHZ2UksFst4XQBDQ0Ns27atoP1v27YNx3EYGxsrRrjLlgaIigTVNcEFT4HquqgjqQxtG92bzHL6bJKDR05EHQYA7WtX0lBX2KJPiUSCtrY24vE4W7duTQ+EHBkZoa1t+V99wu/eGB0d5etf//qs8p6eHgYHB9NjOfw6hbZs+I8fHR3NGGxabpRsiARdcDW85XtRR1E5Hvg2PDoGN/5R1JEsOwePnOBF/3B71GEA8KW33cg1l7QUVMcfuzAwMJAxhmHfvn1s3bq12CEWxejoaEZrRWdnZ0YyEbR9+3YGBgYYGxujo6ODRCKxLLsvlgslGyISnYe+Dwc+pmQjh/a1K/nS226MOgzAjaVQfoJx66230t/fn94+OjpKd3d3+v7Y2Bg7duxg8+bN9PX1hfKBnW+Lw+bNm/OeitrR0ZGeYeJPh13MNFZ/3Ee5JypKNkSCjvwMBp8Nr/tv2PC0qKORCtZQV11wa8Jy449FCLZsZN/v6OjAcZzQEg2Am2++mQMHDhR9v729vezevTs9q2QxRkdHATfRKWcaICqSbeY0pGaijkLkvJfdtTAyMjJrXII/TTOsRGNsbCy0fff09KQTpXynu2YbHBykv7+/4LEe5xslGyJBVZqNIlIssVgs40N0cHBw1oeyvxqnL5FIMDAwwMDAAMPDw/T19aVnavhlw8PD6dU4E4kEQ0ND6fv+hz+4icbu3btpa2tjZGQklNfX2dnJ8PDwogZ3DgwM4DhOznEhS1knY7mtsQHqRhHJZLz8W+tslMZF18CTfyfqKCQk8XiczZs3pwddjo2NsX379ozH7N+/P2PAqD+wdPfu3ezcuZOOjg76+/sZHByku7s73R3iJxnxeJzOzs70KpzBpMJPAObrokkkEgwPDzM6OppOZnp6evJuaejt7SWRSMy5b388RzweZ2BgAHCXK3cch/b29lmrfPrx3HrrrYyNjdHX18eWLVtyrlY612tZTN2wablykSDnYfjba+HV/wlX3Bx1NFLmKm258tbWVo4dO5axbdOmTdxyyy0ZLQP+tUS6uroYHh5m3759bNq0iYMHD6YHm/b29rJ161a6uroYGBggFovR09NDb28v3d3d6daS9vZ2Dh48WLoXeR7RcuUiUVl5EbzpuxocWionj8J47m+Fcv5rbW1ND5yca8Erf+po0P79+zNms/hdJO3t565XNDo6mv7GHnz8yMgImzdvZmRkhEQikd53GN0okj91o4gE1dS5TftSGj8agrFPwrt+GnUkUmSO47Br1y5isRiO4zA4OJgxI8TvYojH47MSjkQiwZ49ewB3PQt/munu3bsZHh4mkUhkLLS1ffv29AXQOjo62LNnD9u2bSMWi9HW1raolT2luNSNIhI0dRy+9qew5Y2w7qlRR1P+/nd3RScb5d6NMjQ0RFtbG4lEIu9xEI7jsGPHjllXQpXiK2U3ilo2RIKS0zD2CXjSViUbIku0mEWuRkZG2LJlSwjRSJSUbIgE+bNRtM6GSCT8rhUpL0o2RIK0zkZpGXMuwROBWVNBpTzor1wkyL/EvE1FG0eleM6fwDt/EnUUIhIyJRsiQdV1cMMfwporo45ERKRsKNkQCaqugef9BVz861FHUhlGPwaf1pREkXKnZEMk20Pfg+OHoo6iMhx/DB6/J+ooRCRkSjZEsn3st+H+r0YdhYhI2dBsFJFsVdW6EJtIhfBXMvWvx+IvjX7w4EHGx8fZvn17eln04eHh9IXVOjs76e/vn7XyaXd3N47j0NvbS1dXV859Hz16FHCXX1/MWiTnIyUbItlMtaa+ilSIeDxOf38/Y2NjxOPxWZd7b29vJ5FIsHPnTrq6uujs7KS1tZXe3t5Z13SJx+PpJdr9a7XMt2//onHZq6U6jsOePXvYu3dvwVOB/SvL+hefGxwcLKh+WNSNIpKtoRVOH1v4cbJ0T34p/PZfRR2FyJx6e3vp6+tL34/FYukWi1wSiUQ60VjI4OAgjuMwNDSU3jY2NsaePXtwHIfx8fGCYu3r62Pnzp3s3LkzHd/WrVsL2kdYlGyIZFvzJK2zUSoXPgWufmHUUYjMyb+ei3/1WnATkJGRkYxt2Y/PV3d3d0Yy09HRQU9PT8GrqDqOw9jYWM44E4nor6ysZEMk2+u/BL/57qijqAy/3A9j/x51FBKSkZERhoeHM765O47Dpk2bIoyqMAcOHKCjoyMjiejs7CQWi2W8LmBRV5fdtm1bOlFYqtHR0YzEwk9YciVFpaZkQ0Si8/Pb4JsfjDoKCUEikaCtrY2Ojg76+/vT20dGRmhra4swsvw4jsPAwACjo6MZl7P39fT0zOpKcRyn4JYN//Gjo6OLDTW9n2PHjmWMIxkZGQFYFtea0QBRkWw/ugV+NARv3R91JFLpjh+aveZLQwxaL4fpM3Dkvtl1/AXpnvg5nD2ZWRa7FBrb4OQTMPFIZln9Kljd7g6OPnT3ue2rLnJvBfLHLgwMDGSMYdi3b9+yGUeQbXR0NKO1orOzc9agTt/27dsZGBhgbGyMjo4OEonEsvhQD9q9ezeDg4MFJ0BhULIhkq261j1RJ2fcFUVFojL6MfhWVsvPtdvgFbfA5KMwdNPsOu+bcH9+4c3wSFbC/LIhuG47/OTz8JU/zixrfy685vNughLc701/Ar+5q+DQ/QTj1ltvzWjZGB0dpbu7O31/bGyMHTt2sHnzZvr6+kL5wM63xWHz5s15T0Xt6OggHo8zODiYng67mGmsfhdHsV93X18f27dvXzZTa3UmFcm2ah1g4eRhaL446mikkm3+PbjqtzK3NcTcn82XQM+35q77O/+Su2UD4Ckvg/VbMsvqV7k/65oy97uIVg2fPxYh2LKRfb+jowPHcUJLNABuvvlmDhw4UPT99vb2plsPFjsuwu8+2bx5c9HiGh4eXnZreCjZEMnmn1yPP6ZkI2yr22Hjs6KOYvmarwujdsX81/BZ86S5y5rWuLdcqqqLdm2g7K6FkZGRWWtT+FM8w0o0/DUuwtDT00NfX1+6FWExBgcH6e/vL1pXhz9Ow080wj6++dIAUZFsq9a5P3V9lPBd90p42UeijkJCEovFMj5EBwcHZ61B4a/G6UskEgwMDDAwMMDw8DB9fX3pmRp+2fDwcHo1zkQiwdDQUPq+30oCbqKxe/du2tra0h/CxX59nZ2dDA8Pz0qi8jEwMIDjODnHhRS6xga4rzc4hsQ/NsthQK5aNkSyNa6BN3wN1l4ddSTlb/o0JM/CipaoI5EQxONxNm/enB50OTY2NqsFYP/+/RkDRv2Bpbt372bnzp3p2SyDg4N0d3enu0P8D9J4PE5nZ2d6Fc5gUuEnAPN10SQSCYaHh9PTRgcGBujp6cm7paG3t3fOdSz8pdBHRkaIx+Pp1T2PHj2K4zi0t7fPWiHUj+fWW29lbGyMvr4+tmzZkl4yfS6O43DzzTdnJFu+uQa5lpKx1kYdQ6iMMc3AxMTEBM3NzVGHIyJB3/hLuPM/4B2VeeXXM2fO8MADD7Bx40ZWrFgRdTiha21t5dixzNV5N23axC233JLRMuBfS6Srq4vh4WH27dvHpk2bOHjwYHqwaW9vL1u3bqWrq4uBgQFisRg9PT3pJcD91pL29vb00t2SKZ/fv8nJSVpaWgBarLWTi32ugrtRjDE9gVte6dJCdZZaLlJ0P7oFDnwi6ihEzmutra3pgZNzLXjlN/sH7d+/P2M2i99F0t7enn7M6Oho+tt+8PEjIyNs3rw5vXKmv+8wulEkfwV1oxhjeoCYtXbAu99ljOm31vYtts5Sy0VCkfgmzEzBptdFHYnIeclxnPRFyRzHYXBwMGNGiN/FEI/HZyUciUSCPXv2AO56Fv400927dzM8PEwikchYaGv79u0MDw8Ti8Xo6Ohgz549bNu2jVgsRltb26JW9pTiKqgbxRhzENhqrU0Eth2z1rYuts5Sy/OIWd0oUrgvvwse/gG8+btRR1Le1I1S1t0o/uDERCKR9zgIx3HYsWPHrCuhSvGVshsl75YNY0wMiAc/9D0xY0yHtXbWwu4L1QESSynP9ZwiRbHqIneFxeS0u8iXiBRsMes8jIyMsGXLloUfKOeVQrpR5pqk63hluT74F6ozl3zLlWxIODbeBN/sh31/Di/YDVMn3FkTQTX17gJIyWmYOp5ZZqrOLb50ZnL2VWTrmtwkZvq0210TVF3rlqeSs/cL5/Y7ddx9TFBtI9TUufucPp1ZVlUD9SvBWjgzMXu/9c1QVeW+1tRM1n4b3Nc7cxamT2Xtt/rcglCnnRz7XeU+5uyp2cfwN94Cz3jr7DpSsfyuFSkvhSQbc03UHZ+nbKE6zhLLZzHG1AP1gU2r5tiHyNw2PA1e+wVY7S2MdNufwYGPZT7mGW+F5/9fePQA/NvzM8uaLoD/7+fu/z/yTHAezix/zefd5aG/82H49kBm2XWvcteeGH8A/jH76pgG3ue4//3kS93nDnr5v8JTu+HAx+GrWWOpr+iEV3/OTTT6L5v9mnc+4F43Y/gN8POvZZY9fzc84y1w3xfd8qB1vw693oqTf9U+O1H5gx/B2qvc5bHv+HRm2bPeBTf/+exYpGJlTwWV8lCO62zsAt4bdRBSBi6/8dz/N7/B/bAOatvo/lxzJWzLukx6TSDffeGHZ7cGXHiN+/Oal8NF12SWtax3f666ELZ9cu74bn4vnHEyt13sDbK7onN23aYL3J+1jbn3W9fk/nzWO+H6380d74anz64bXCOj62NA1jgwf5G0LW+EK7OSstXzrHIpImUj7wGi3hiKA9Zak7X9GLDDWjtcaB3cMRmLLp/jOXO1bDyiAaIistyU+wBRWd6W6zobCUgP+gyK+WWLqLPU8lmstVPW2kn/BuTo9BYRWT7KfXFFWZ5K+XuXd7JhrXVwP+BnjZWYa1bIQnWWWp5v7CIiy1FtbS3GGE6ePLnwg0WK7NQpt3u3tjb8GXeFjtnoB3qB4IJb6cW1jDFxoNNaO5RvnSKUi4icl6qrq2lpaeHIkSNMTU3R3NxMTU0NxpiFK4sskrWWU6dOcfjwYWKxGNXV1aE/Z8HXRvGWC3dwuzJWB1fy9BMBa217vnWKUb5AvFrUS0SWLWstExMTHD58mGQyuXAFkSKJxWJcdNFF8ya3xRqzoQuxiYgsA9ZakskkMzMzCz9YZIlqa2vzatEo+QqiIiISHmMMNTU11NTotCzlp+CrvoqIiIgUQsmGiIiIhErJhoiIiIRKyYaIiIiEqmJGIk1OLnoQrYiISEUq1mdnJSQbbQAbNmyIOg4REZHzVRugqa/zGPd+rkfXSSmVVcAj6JiXko556emYl56Oeen5x3x8oQfOpxKSDd/xpSxIIvkLrEanY14iOualp2NeejrmpVespfM1QFRERERCpWRDREREQlUJycYU8P97P6U0dMxLT8e89HTMS0/HvPSKcszL/kJsIiIiEq1KaNkQERGRCCnZEBERkVAp2RAREZFQVdI6GyJlzxizz1q7Neo4ROT8Y4zpBHqttd05ynoCd2PW2oFC9l3WycZSD44Uzhiz0/vvFiBhre2LMp5KYozpAjqjjqNSGGP6gYPe3XFr7XCU8ZS7wPk8BqwGdltrncgCKiNekrEV99jGc5T3EPgMNcZ0GWP6Czm/l+1slFwHB9iiD7/wZP/yGWP2AuTKkqW4jDExoAfot9YWZ8k/mZMxZh/uN8CEMaYDOKDjHh7vS8ywtTbh3Y/h/q73RhpYmfE+J3dZazdlbT8IbPWPv7ftmLW2Nd99l/OYjT4g/U3D+9bRM/fDZSm8P/5O76dvN9BljJmVKUvRbQOGog6iEnhfZMb8E6+1dgzYNH8tWaKMDzqvRUPnlRLwzunx4PH3xLxEOy9lmWwU6+BIweJkngASge0SEu93ejTqOCpIP7AvuMFLOCQ8bYEuWimtuc7fzjxls5TrmI2FDo5ODEXmfdPIblLz34fspE+Ka7O1diirVUlC4B3jGO4XF7+ltF3ds6HrA/YZY7YCvYGbhK9tju3j85TNUpYtGxTp4MiS9QIjOVqYpEiMMV3WWnWflI6fQLdZa4e8Y7/PH58k4bDWjuAOYOzEHZS7X+eV80u5JhsSMa9pvxPQ4NCQeGNhnKjjqDD+l5V0t5X3QaixSSHyjm0HbuvpELBX3SolMz7H9rZ5ymYp126UohwcWZJ+YJOmpoWqE2j3mpbBbd73p2Tu11TMUCSyfgZ1zLFdlq4/MKut15sNtNcYM6wWjtClZwBlnc9jFPD7Xq7JRlEOjiyOMWYQd1qgE3Us5Sy7+8RrTerR+IHweFNdQWO/Ssb7vc44b1trh40xA7gJt7oRQ2StdYwxCdwv605WWd5/A2XZjeJ9yPkHJ7tMJ4gQeYPm+gPz4Ts0A0jKzBi5B6Hr3FJaB9GXx2Kba0xjP4EBud55vqAvNWWZbHiWfHCkMN6CMDEgbozp9O73ohNC6Pwkz/v/Xm9FQAlHH+5gRSB97NWcHxLvC2JHjjExm7zxMrJE3pfCftzf7Q5jzGBwBW6vFfWoMabHGyvTXuiK3GW7giikV51z8Ja3VfNyeLwpgcdylWllRSk33om43b+vc0u4vPPLLu/uUbRc+XmnrJMNERERiV45d6OIiIjIMqBkQ0REREKlZENERERCpWRDREREQqVkQ0REREKlZENERERCpWRDRM5LxpiYMeaAt4hZbJ7H9XiP0YW7RCKidTZEJM1bpfEgMMK55bd34q4CO4y7QN5mIG6tbfXq9Hv3S3qFX3+hp3wX1DLGDFprexd+pIgUW7leiE1EFicG9AWXIvaWPh8JfqgbY/YG6uzHXdVRRCQnJRsiEhTHbcFYyD7/P7qUvYgsRGM2RCRDnhcUS8w3TkJEJEgtGyKSlm8rhX+1TWNMB3CLt22Tt60T7wq0wA7c1pI23Kt09noXMRsHtuNeTCvj0uzeGJD9Xj3Hu+JkXrxBoH6yVHB9EQmHkg0RWTRr7Zgxpg8YDGwbCWxr8xMYY8xBY0y/P/bDGANuorLJr2uM2Qf0B5KZvcaYRD6XEjfGdOEmF/7zxYGuIr1UEVkCdaOIyFKNz7EtnpUkZHfPJHBbH4B0K0lnVp19QL4zSNqAbr97x+sOWjBJEZHwqWVDRMLi5Nh2cJ7yzbhjQYKtETHcLpUFWWuHjDHdwDFjzBhwa3BWjYhER8mGiCwrS5ndYq3d6nWfdAJ9xpjV+a7DISLhUTeKiCwXowS6VXxe8rAgb6XQmLU24Q0K3YSbdIhIxJRsiMhCYt4tjP2mebNShr3ZKkB6ldCOAvbXk7Utn2m8IhIydaOISE7+MuTebZs3e+RAcCqpN6izH4j7y4EHtsWMMYNAH7ALt5UhbowZx00CMh5jrXWstd3GmH5vCqsD7liMPEN2vJj8MR9x3Km3IhIxXRtFRM5LujaKyPlD3SgiIiISKiUbIiIiEiolGyIiIhIqJRsicj7r9JY0j831AG9K7F4yFxQTkRLSAFEREREJlVo2REREJFRKNkRERCRUSjZEREQkVEo2REREJFRKNkRERCRUSjZEREQkVEo2REREJFRKNkRERCRU/w+piOeOGOu1BAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Figure size 600x400 with 1 Axes>,\n",
+       " <AxesSubplot:title={'center':'PVD1 Iq\\\\_out (actual output current)'}, xlabel='Time [s]'>)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss.TDS.plt.plot(ss.PVD1.Ipout_y,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 Ip\\_out (actual output current)')\n",
+    "\n",
+    "ss.TDS.plt.plot(ss.PVD1.Iqout_y,\n",
+    "                a=(0,1),\n",
+    "                title='PVD1 Iq\\_out (actual output current)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r\n",
+      "    _           _         | Version 1.3.12.post66.dev0+gefb90346\r\n",
+      "   /_\\  _ _  __| |___ ___ | Python 3.7.10 on Linux, 09/02/2021 04:38:24 PM\r\n",
+      "  / _ \\| ' \\/ _` / -_|_-< | \r\n",
+      " /_/ \\_\\_||_\\__,_\\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.\r\n",
+      "\r\n",
+      "No output file found in the working directory.\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!andes misc -C"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "22e39ba7dda51165b7745563f4e2534bee0d38fdb3ee17f5faee115ef7f334ee"
+  },
+  "kernelspec": {
+   "display_name": "andes",
+   "language": "python",
+   "name": "andes"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "7dcf7be8cc97f6f28812ee601415372b88ddefbec529c6a68a6def4717df442e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 2075d9abb..40f557c5b 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -11,3 +11,4 @@ numpydoc
 sphinx-copybutton
 sphinx_rtd_theme
 myst-parser
+pre-commit


Test patch:
diff --git a/tests/test_case.py b/tests/test_case.py
index 4fde05c2a..5d4a45bb2 100644
--- a/tests/test_case.py
+++ b/tests/test_case.py
@@ -249,6 +249,23 @@ def test_kundur_COI_empty(self):
         self.assertEqual(exit_code, 0, "Exit code is not 0.")
 
 
+class TestVSC(unittest.TestCase):
+    """Test case =for VSC power flow model"""
+
+    def test_kundur_vsc(self):
+        """Test power flow exit code"""
+
+        ss = get_case('kundur/kundur_vsc.xlsx')
+        exit_code = andes.run(ss,
+                              routine='pflow',
+                              no_output=True,
+                              cli=True,
+                              default_config=True,
+                              )
+
+        self.assertEqual(exit_code, 0, "Exit code is not 0.")
+
+
 class TestShuntSw(unittest.TestCase):
     """Test class for switched shunt."""