Cloud-based hybrid state estimation

Systems, methods, techniques and apparatuses of power network state estimation are disclosed. One exemplary embodiment is a method for state estimation in a power network comprising receiving a set of supervisory control and data acquisition (SCADA) information including a power network topology; generating a SCADA state estimation using the set of SCADA information; receiving, with a cloud-computing architecture, a set of PMU phasors; aligning, with the cloud-computing architecture, a timestamp of the SCADA estimation and a timestamp of the set of PMU phasors; updating, with the cloud-computing architecture, the power network topology using the set of PMU phasors; generating, with the cloud-computing architecture, a hybrid state estimation using the updated power network topology, the set of PMU phasors, and the SCADA state estimation; and transmitting the hybrid state estimation to a local control system.

BACKGROUND

The present disclosure relates generally to power network state estimation. Energy management system applications use state estimations for control and protection of the power network. Conventional state estimation includes using supervisory control and data acquisition (SCADA) measurements and network topology data to generate a SCADA state estimation at regular intervals, such as 5-15 minute intervals. Control and protection applications use the generated SCADA state estimation until a new SCADA state estimation is generated during the next interval. Existing power network state estimation suffers from a number of shortcomings and disadvantages. There remain unmet needs including increasing state estimation accuracy and increasing state estimation response to significant changes in the power network. Waiting several minutes for a new state estimation may jeopardize the health and efficiency of the power network. As more low-inertia power generation systems are added to the power network, the likelihood of sudden shifts in power generation increases. For example, a change in cloud cover or a change in wind speed would alter the actual states of the power network such that the current SCADA state estimation is no longer an accurate representation of the power network. Furthermore, the opening of a circuit breaker, or other changes in network topology, would also cause the actual states of the power network to deviate from the current SCADA state estimation. In view of these and other shortcomings in the art, there is a significant need for the unique apparatuses, methods, systems and techniques disclosed herein.

DISCLOSURE OF ILLUSTRATIVE EMBODIMENTS

For the purposes of clearly, concisely and exactly describing non-limiting exemplary embodiments of the disclosure, the manner and process of making and using the same, and to enable the practice, making and use of the same, reference will now be made to certain exemplary embodiments, including those illustrated in the figures, and specific language will be used to describe the same. It shall nevertheless be understood that no limitation of the scope of the present disclosure is thereby created, and that the present disclosure includes and protects such alterations, modifications, and further applications of the exemplary embodiments as would occur to one skilled in the art with the benefit of the present disclosure.

SUMMARY OF THE DISCLOSURE

Exemplary embodiments of the disclosure include unique systems, methods, techniques and apparatuses for power network state estimation. Further embodiments, forms, objects, features, advantages, aspects and benefits of the disclosure shall become apparent from the following description and drawings.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

With reference toFIG.1, there is illustrated an exemplary state estimation system100for a power network. System100is structured to generate a new state estimation and a network topology in near real-time. For example, system100may generate a new state estimation at least every second or every half-second, to name but a few examples. It shall be appreciated that system100may be implemented in a variety of power networks, including power transmission systems and power distribution systems, to name but a few examples.

System100includes a plurality of remote terminal units (RTUs)110, a plurality of phasor measurement units (PMUs)130, a plurality of phasor data concentrators (PDCs)120, a local control system (LCS)170, and a cloud-computing architecture180. It shall be appreciated that the topology of system100is illustrated for the purpose of explanation and is not intended as a limitation of the present disclosure. For example, an exemplary state estimation system may include more or fewer RTUs, PDCs, or PMUs, to name but a few examples.

Each of the plurality of RTUs110are structured to receive SCADA information corresponding to characteristics of the power network from a plurality of sensors or meters and transmit the SCADA information to local control system170. The SCADA information may be transmitted to the plurality of RTUs continuously and transmitted to local control system170in response to being polled by local control system170. The SCADA information may be received from intelligent electronic devices (IEDs), relays, sensors, or other devices structured to monitor the power network. The SCADA information may include measurements such as voltage measurements, current measurements, or power measurements. For example, the measurements may include bus voltages, real power injection, reactive power injection, and line flow. The SCADA information may also include a network topology including a plurality of on/off statuses for controllable switches, including circuit breakers, within the power network. In certain embodiments, the plurality of RTUs110are structured to receive instructions from local control system170and operate controllable devices of the power network in response to receiving the instructions. The controllable devices may include controllable switches such as circuit breakers or disconnectors, to name but a few examples.

The plurality of RTUs110and local control system170communicate by way of an RTU/LCS communication network160using a communication protocol. For example, the plurality of RTUs110and local control system170may use a communication protocol based on distributed network protocol (DNP3), the IEC 60870-5-101 standard, or the IEC 60870-5-104 standard.

The plurality of PMUs130is structured to synchronize measured electrical characteristics of the power network using a common time source and output synchronized phasors, also known as synchrophasors, corresponding to the measured electrical characteristics. The phasors may correspond to a voltage magnitude and phase angle or a current magnitude and phase angle. For example, a PMU may output a voltage phasor based on measurements of a bus or output a current phasor based on measurements of current through a distribution line. In certain embodiments, some of the PMUs may be replaced with other devices having the PMU functionality described above, such as an IED or a protective relay. Each of the plurality of PMUs130transmits the phasors to one of the plurality of PDCs120.

Each PDC of the plurality of PDCs120is structured to communicate with multiple PMUs of the plurality of PMUs130. In the illustrated embodiment, each PDC of the plurality of PDCs120aggregates phasors from the multiple PMUs, aligns the phasors into sets of PMU phasors based on the timestamps of each phasor, and transmits aligned sets of PMU phasors to local control system170. In certain embodiments, one or more of the plurality of PDCs120transmits aligned sets of PMU phasors directly to a cloud PDC application183of cloud-computing architecture180. In certain embodiments, each PDC transmits sets of PMU phasors at the same frequency as the phasors are received by the PDC. For example, each PDC transmits sets of PMU phasors at a rate of 60 sets of PMU phasors per second or 30 sets of PMU phasors per second, to name but a few examples.

The plurality of PMUs130and the plurality of PDCs120communicate by way of a PMU/PDC communication network140using a communication protocol. For example, the plurality of PMUs130and the plurality of PDCs120may use a communication protocol based on the IEEE c37.118 standard, to name but one example.

The plurality of PDCs120and local control system170communicate by way of a PDC/LCS communication network150using a communication protocol. For example, the plurality of PDCs120and local control system170may use a communication protocol based on the IEEE c37.118 standard, to name but one example.

Local control system170includes an input/output device179, a processing device177, and a memory device171. Local control system170may be a stand-alone device, an embedded system, or a plurality of devices structured to perform the functions described with respect to local control system170. For example, local control system170may be an energy management system (EMS).

Input/output device179enables local control system170to communicate with a plurality of external devices including the plurality of RTUs110, the plurality of PDCs120, and cloud-computing architecture180. Input/output device179may include a network adapter, network credential, interface, or a port (e.g., a USB port, serial port, parallel port, an analog port, a digital port, VGA, DVI, HDMI, FireWire, CAT 5, Ethernet, fiber, or any other type of port or interface), to name but a few examples. Input/output device179may be comprised of hardware, software, and/or firmware. It is contemplated that input/output device179includes more than one of these adapters, credentials, or ports, such as a first port for receiving data and a second port for transmitting data.

Processing device177is structured to execute applications stored on memory device171. Processing device177may be a programmable type, a dedicated, hardwired state machine, or a combination thereof. Processing device177may include multiple processors, Arithmetic-Logic Units (ALUs), Central Processing Units (CPUs), Digital Signal Processors (DSPs), or a Field-programmable Gate Array (FPGA), to name but a few examples. For forms of processing device177with multiple processing units, distributed, pipelined, or parallel processing may be used. Processing device177may be dedicated to performance of just the operations described herein or may be used in one or more additional applications. In the illustrated form, processing device177is of a programmable variety that executes processes and processes data in accordance with applications including sets of instructions stored in memory device171. Alternatively or additionally, programming instructions are at least partially defined by hardwired logic or other hardware. Processing device177may be comprised of one or more components of any type suitable to process the signals received from input/output device179or elsewhere, and provide output signals. Such components may include digital circuitry, analog circuitry, or a combination of both.

Memory device171is structured to store supervisory control and data acquisition (SCADA) information, phasor data, and a plurality of applications including a SCADA master application175, a data storage application172, a state estimation application173, and a super PDC application174. Memory device171may be of one or more types, such as a solid-state variety, electromagnetic variety, optical variety, or a combination of these forms, to name but a few examples. Furthermore, memory device171may be volatile, nonvolatile, transitory, non-transitory, or a combination of these types, and some or all of memory device171may be of a portable variety, such as a disk, tape, memory stick, or cartridge, to name but a few examples.

SCADA master application175includes instructions executable by processing device177effective to poll the plurality of RTUs110, receive SCADA information including measurements and a network topology from the plurality of RTUs110, timestamp the SCADA information, and transmit the received SCADA information to state estimation application173and data storage application172. In certain embodiments, SCADA master application175transmits the SCADA information to state estimation application173every 5-15 minutes.

State estimation application173includes instructions executable by processing device177effective to generate a SCADA state estimation of the power network using SCADA information received from SCADA master application175. State estimation application173may generate the SCADA state estimation using weighted least squares, weighted least absolute values, or extended Kalman filters, to name but a few examples. Once the SCADA state estimation is generated, it is timestamped by application173. In certain embodiments, state estimation application173generates a SCADA state estimation every 5-15 minutes.

Super PDC application174includes instructions executable by processing device177effective to receive phasors from each of the plurality of PDCs120, align the phasors using the timestamps of the phasors, and transmit sets of aligned phasors to a cloud PDC application183of cloud-computing architecture180as well as data storage application172. In certain embodiments, super PDC application174receives phasors from the plurality of PDCs120at a rate of 30-120 times per second.

Data storage application172includes instructions executable by processing device177effective to archive phasors received by super PDC application174, archive the SCADA state estimations generated by state estimation application173, and archive SCADA information received by SCADA master application175. For example, data storage application172may maintain six months of historical values, to name but one example.

Cloud-computing architecture180is a system of scalable system resources with on-demand availability. Cloud-computing architecture180includes an input/output device189, a processing device187, and a memory device181. In certain embodiments, cloud-computing architecture180is a virtualized platform with a cloud broker structured to allocate scalable computing resources.

Input/output device189enables cloud-computing architecture180to communicate with local control system170. For example, input/output device189may include a network adapter, network credential, interface, or a port (e.g., a USB port, serial port, parallel port, an analog port, a digital port, VGA, DVI, HDMI, FireWire, CAT 5, Ethernet, fiber, or any other type of port or interface), to name but a few examples. Input/output device189may be comprised of hardware, software, and/or firmware. It is contemplated that input/output device189includes more than one of these adapters, credentials, or ports, such as a first port for receiving data and a second port for transmitting data.

Processing device187includes multiple processing units using distributed, pipelined, or parallel processing. In the illustrated form, processing device187is of a programmable variety that executes applications in accordance with programming instructions (such as software or firmware) stored in memory device181. Processing device187may be comprised of one or more components of any type suitable to process the signals received from input/output device189or elsewhere, and provide output signals. Such components may include digital circuitry, analog circuitry, or a combination of both.

Memory device181is structured to store SCADA information, PMU phasors, and a plurality of applications including a cloud PDC application183, a topology checker application184, a hybrid state estimation application182, and a data storage application185. Memory device181may be of one or more types, such as a solid-state variety, electromagnetic variety, optical variety, or a combination of these forms, to name but a few examples. Furthermore, memory device181may be volatile, nonvolatile, transitory, non-transitory, or a combination of these types, and some or all of memory device181may be of a portable variety, such as a disk, tape, memory stick, or cartridge, to name but a few examples.

Cloud PDC application183includes instructions executable by processing device187effective to receive phasors from super PDC application174. In certain embodiments, cloud PDC application183may also perform monitoring functions such as visualization of power network events, to name but one example.

Topology checker application184includes instructions executable by processing device187effective to determine power network topology using the PMU phasors received by cloud PDC application183. If topology checker application184detects any changes to the on/off statuses of the controllable switches of the power network, the application184updates the power network topology before transmitting the updated power network topology to hybrid state estimation application182. In certain embodiments, topology checker application184is structured to determine a power network topology by generating a new power network topology using the PMU phasors. Since the PMU phasors correspond to more recent measurements of the power network, the generated power network topology will reflect any updates in on/off statuses of switches in the power network. In certain embodiments, application184is configured to determine power network topology by comparing the PMU phasors to the network topology transmitted by SCADA master application175to determine if any on/off statuses of controllable switches of the power network have changed since the most recent SCADA state estimation. For example, a zero current phasor for a distribution line indicating a circuit breaker has been opened indicates a change in power network topology where the network topology received from SCADA master application175included a closed status for the circuit breaker.

In other embodiments, topology checker application184is configured to generate a network topology using the current set of PMU phasors and compare the generated network topology to a network topology generated using the previous set of phasors or the network topology transmitted by SCADA master application175. In response to determining the network topology based on the current set of PMU phasors includes updates to the power network topology, the updated network topology is transmitted to hybrid state estimation application182. By using each new set of aligned phasors to update the network topology, cloud-computing architecture180is configured to output a hybrid state estimation reflecting changes in network topology in near real-time.

Hybrid state estimation application182includes instructions executable by processing device187effective to generate a hybrid state estimation using the SCADA state estimation generated by state estimation application173and the most recently received set of PMU phasors received by cloud PDC application183, as described in more detail below.

Before performing hybrid state estimation, application182aligns the timestamps of the received SCADA state estimation and the set of PMU phasors by identifying the most recent SCADA state estimation and the most recent set of PMU phasors for use in the hybrid state estimation. Once aligned, application182converts any values of the SCADA state estimation and the synchronized phasor data in polar format to rectangular format. Once hybrid state estimation is completed, the estimated states are converted from rectangular format back to polar format.

Hybrid state estimation application182transmits the hybrid state estimation to local control system170for use in network control systems. In certain embodiments, local control system170receives a new hybrid state estimation from cloud-computing architecture180every second or less. In certain embodiments, local control system170receives a new hybrid state estimation every half-second or less.

Data storage application185includes instructions executable by processing device187effective to archive sets of aligned phasors received from local control system170, archive the hybrid state estimations generated by state estimation application182, archive SCADA information received from local control system170, and archive SCADA state estimations received from local control system170. Data storage application185may maintain one year of archived values, to name but one example.

Local control system170and cloud-computing architecture180communicate by way of LCS/Cloud communication network190. Multiple communication protocols may be used to exchange data in LCS/Cloud communication network190. For example, synchronized phasor data may be transmitted from super PDC application174to cloud PDC application183using phasor data transfer protocol, also known as IEEE C37.118 protocol, to name but one example. SCADA state estimations, hybrid state estimations, SCADA information, and archived data may be transmitted between local control system170and cloud-computing architecture180using file transfer protocol (FTP), to name but one example.

With reference toFIG.2, there is illustrated an exemplary process200for state estimation of a power network implemented by an exemplary state estimation system, such as state estimation system100ofFIG.1. It shall be appreciated that a number of variations and modifications to process200are contemplated including, for example, the omission of one or more aspects of process200, the addition of further conditionals and operations, and/or the reorganization or separation of operations and conditionals into separate processes.

Process200begins at operation201where a local control system including a SCADA master station receives SCADA information from a plurality of power network devices. The SCADA information may include measurements and a power network topology. The measurements may include voltage measurements, current measurements, or power measurements. For example, the measurements may include bus voltages, real power injection, reactive power injection, and line flow. The power network topology includes a plurality of on/off statuses for controllable switches of the power network. The power network devices may include remote terminal units (RTUs), intelligent electronic devices (IEDs), relays, sensors, or other devices structured to monitor the power network. The measurements and device statuses of the SCADA information may include a timestamp, but measurements are not synchronized with a common time source.

Process200proceeds to operation203where a state estimator of the local control system generates a SCADA state estimation using the set of SCADA information. The state estimator may use one of a plurality of algorithms to generate the SCADA state estimation, such as weighted least squares, weighted least absolute values, or extended Kalman filters, to name but a few examples.

Process200proceeds to operation205where the local control system transmits, and a cloud-computing architecture receives, the SCADA state estimation.

Process200proceeds to operation207where the cloud-computing architecture receives a set of PMU phasors generated by a plurality of PMUs of the power network. In certain embodiments, the set of PMU phasors are received from a plurality of PDCs of the power network at a cloud PDC of the cloud-computing architecture. In certain embodiments, the set of PMU phasors are received from a super PDC of the local control system, the super PDC having aggregated and aligned the set of PMU phasors from the plurality of PDCs of the power network. Each PMU phasor corresponds to a voltage phasor or a current phasor. The set of PMU phasors are synchronized, and therefore each include the same timestamp.

Process200proceeds to operation209where the cloud-computing architecture aligns the timestamp of the SCADA estimation and the timestamps of the set of PMU phasors by identifying the most recently received SCADA estimation and the most recently received set of PMU phasors.

Process200proceeds to operation211where the cloud-computing architecture determines a current power network topology using the set of PMU phasors received at operation207. In certain embodiments, the cloud-computing architecture uses the received set of PMU phasors to generate an updated network topology. In certain embodiments, the received set of PMU phasors are compared to a previously generated network topology to detect changes in the power network. The cloud-computing architecture then updates the power network topology in response to detecting changes in the power network.

Process200proceeds to operation213where the cloud-computing architecture generates a hybrid state estimation using the determined power network topology from operation211, the set of PMU phasors, and the SCADA state estimation. In certain embodiments, the cloud-computing architecture generates the hybrid state estimation by performing weighted least squares state estimation using the following equation where x is the state estimation vector, A is the function matrix, W is the hybrid weight matrix, and zhybridis the measurement matrix:
x=[ATW−1A]−1[W−1A]zhybrid(1)
Function matrix A includes the following values, where 1 represents a unit matrix, 1′ represents a unit matrix with zeros on the diagonal where no voltage phasors have been measured, and C1-4are matrices comprising line conductances and susceptance for those power lines from which current phasor measurements were received.

A=[10011′001′C1C2C3C4]
zhybridincludes the following values, where Vr(1)and Vi(1)are real and imaginary components of voltage estimation results from the SCADA estimation in rectangular format, Vr(2)and Vi(2)are real and imaginary components of voltage phasor measurements from the set of PMU phasors in rectangular format, and Ir(2)and Ii(2)are real and imaginary components of current phasor measurements from the set of PMU phasors in rectangular format.

zhybrid=[Vr(1)Vi(1)Vr(2)Vi(2)Ir(2)Ii(2)]
W includes the following, where W1is the weight matrix for the SCADA state estimation and W2is the weight matrix for the set of PMU phasors. Each weight matrix may be determined based on an accuracy class of the sensors transmitting measurements to each PMU.

Process200proceeds to operation215where the cloud-computing architecture transmits, and the local control system receives, the hybrid state estimation.

Process200proceeds to operation217where the local control system operates the power network using the hybrid state estimation. In certain embodiments, the local control system may provide the hybrid state estimation to advanced EMS applications. The hybrid state estimation may be used for economic dispatch, protection, and stability analysis, to name but a few examples. Because the hybrid state estimation is updated multiple times during an economic scheduling period, high inertia generation systems may be given more time to prepare to provide power at the next scheduling period and low inertia generation systems, such as solar and wind-based power sources, may be controlled to respond to changes in the power network during a scheduling period. The hybrid state estimation may also be used by a system operator to visualize events that cannot be visualized by the lower frequency SCADA state estimation, such as power swings or inter-area oscillations, to name but a few examples.

Process200proceeds to conditional219where the local control system decides whether it is time to generate a new SCADA state estimation. For example, a new SCADA state estimation may be generated at a SCADA interval of 5-15 minutes. If the time has come to generate a new SCADA state estimation, process200returns to operation203, forming an operational loop223.

If the local control system determines a new SCADA state estimation does not need to be generated, process200returns to operation207, forming an operational loop221. Every time a new SCADA state estimation needs to be generated, process200executes loop223. Within a SCADA interval, process200executes loop221effective to update state estimation and network topology in near real-time.

Further written description of a number of exemplary embodiments shall now be provided. One embodiment is a method for state estimation in a power network comprising: receiving a set of supervisory control and data acquisition (SCADA) information including a power network topology; generating a SCADA state estimation using the set of SCADA information; receiving, with a cloud-computing architecture, a set of PMU phasors; aligning, with the cloud-computing architecture, a timestamp of the SCADA estimation and a timestamp of the set of PMU phasors; determining, with the cloud-computing architecture, the power network topology using the set of PMU phasors; generating, with the cloud-computing architecture, a hybrid state estimation using the determined power network topology, the set of PMU phasors, and the SCADA state estimation; and transmitting the hybrid state estimation to a local control system.

In certain embodiments of the foregoing method, the method comprises generating the hybrid state estimation includes performing weighted least squares states estimation using the following equation and matrices, where x is the state estimation vector, A is the function matrix, W is the hybrid weight matrix, and zhybridis the measurement matrix, 1 represents a unit matrix, 1 represents a unit matrix with zeros on the diagonal where no voltage phasors have been measured, and C1-4are matrices comprising line conductances and susceptance for those power lines from which current phasor measurements were received.

x=[AT⁢W-1⁢A]-1⁡[W-1⁢A]⁢zhybridA=[10011′001′C1C2C3C4]
In certain forms, the measurement matrix includes the following, where Vr(1)and Vi(1)are real and imaginary components of voltage estimation results from the SCADA estimation in rectangular format, Vr(2)and Vi(2)are real and imaginary components of voltage phasor measurements from the set of PMU phasors in rectangular format, and Ir(2)and Ii(2)are real and imaginary components of current phasor measurements from the set of PMU phasors in rectangular format.

zhybrid=[Vr(1)Vi(1)Vr(2)Vi(2)Ir(2)Ii(2)]
In certain forms, the set of SCADA information includes voltage measurements of the power network and the power network topology includes on/off statuses of circuit breakers of the power network. In certain forms, generating the SCADA estimation is performed by the local control system. In certain forms, the method comprises iteratively performing the steps of receiving a new set of PMU phasors, aligning the SCADA estimation timestamp and the timestamp of the new set of PMU phasors, determining the power network topology using the new set of PMU phasors, generating an updated hybrid state estimation using the SCADA state estimation and the new set of PMU phasors, and transmitting the updated hybrid state estimation until the local control system transmits a second SCADA state estimation to the cloud-computing architecture. In certain forms, the step of transmitting the updated hybrid state estimation is performed at least once per second. In certain forms, the step of transmitting the updated hybrid state estimation is performed at least twice per second. In certain forms, determining the power network topology includes detecting a change of the topology of the power network using the set of PMU phasors, and updating the power network topology to include the detected change. In certain forms, determining the power network topology includes updating an on/off status of a circuit breaker of the power network topology in response to comparing the set of PMU phasors to the power network topology.

Another exemplary embodiment is a state estimation system for a power network comprising: a local control system configured to receive a set of supervisory control and data acquisition (SCADA) information including a power network topology, transmit a SCADA state estimation generated using the set of SCADA information; and a cloud-computing architecture configured to receive a set of PMU phasors, align a timestamp of the SCADA estimation and a timestamp of the set of PMU phasors, determine the power network topology using the set of PMU phasors, generate a hybrid state estimation using the determined power network topology, the set of PMU phasors, and the SCADA state estimation, and transmitting the hybrid state estimation to a local control system.

In certain forms of the foregoing state estimation system, generating the hybrid state estimation includes performing weighted least squares states estimation using the following equation and matrices, where x is the state estimation vector, A is the function matrix, W is the hybrid weight matrix, and zhybridis the measurement matrix, 1 represents a unit matrix, 1′ represents a unit matrix with zeros on the diagonal where no voltage phasors have been measured, and C1-4are matrices comprising line conductances and susceptance for those power lines from which current phasor measurements were received.

x=[AT⁢W-1⁢A]-1⁡[W-1⁢A]⁢zhybridA=[10011′001′C1C2C3C4]
In certain forms, the measurement matrix includes the following, where Vr(1)and Vi(1)are real and imaginary components of voltage estimation results from the SCADA estimation in rectangular format, Vr(2)and Vi(2)are real and imaginary components of voltage phasor measurements from the set of PMU phasors in rectangular format, and Ir(2)and Ii(2)are real and imaginary components of current phasor measurements from the set of PMU phasors in rectangular format.

zhybrid=[Vr(1)Vi(1)Vr(2)Vi(2)Ir(2)Ii(2)]
In certain forms, the set of SCADA information includes voltage measurements of the power network and the power network topology includes on/off statuses of circuit breakers of the power network. In certain forms, generating the SCADA estimation is performed by the local control system. In certain forms, the cloud-computing architecture is configured to iteratively generate a new hybrid state estimation each time the cloud-computing architecture receives a new set of PMU phasors using the new set of PMU phasors and the SCADA state estimation until the local control system transmits a second SCADA state estimation to the cloud-computing architecture. In certain forms, the cloud-computing architecture generates a new hybrid state estimation at least once per second. In certain forms, the cloud-computing architecture generates a new hybrid state estimation at least twice per second. In certain forms, determining the power network topology includes detecting a change of the topology of the power network using the set of PMU phasors, and updating the power network topology to include the detected change. In certain forms, determining the power network topology includes updating an on/off status of a circuit breaker of the power network topology in response to comparing the set of PMU phasors to the power network topology.

While the present disclosure has been illustrated and described in detail in the drawings and foregoing description, the same is to be considered as illustrative and not restrictive in character, it being understood that only certain exemplary embodiments have been shown and described, and that all changes and modifications that come within the spirit of the present disclosure are desired to be protected. It should be understood that while the use of words such as “preferable,” “preferably,” “preferred” or “more preferred” utilized in the description above indicate that the feature so described may be more desirable, it nonetheless may not be necessary, and embodiments lacking the same may be contemplated as within the scope of the present disclosure, the scope being defined by the claims that follow. In reading the claims, it is intended that when words such as “a,” “an,” “at least one,” or “at least one portion” are used there is no intention to limit the claim to only one item unless specifically stated to the contrary in the claim. The term “of” may connote an association with, or a connection to, another item, as well as a belonging to, or a connection with, the other item as informed by the context in which it is used. The terms “coupled to,” “coupled with” and the like include indirect connection and coupling, and further include but do not require a direct coupling or connection unless expressly indicated to the contrary. When the language “at least a portion” and/or “a portion” is used, the item can include a portion and/or the entire item unless specifically stated to the contrary.