Patent Publication Number: US-2023145642-A1

Title: Framework for fault detection and localization in power distribution networks

Description:
RELATED APPLICATIONS 
     This application is a continuation of U.S. non-provisional patent application Ser. No. 16/702,049, filed Dec. 19, 2019, which is based on, and claims benefit of, U.S. non-provisional patent application Ser. No. 15/088,971, filed Apr. 1, 2016, which is based on, and claims the benefit of, U.S. provisional application 62/142,791, filed Apr. 3, 2015, each of which is incorporated herein by reference in their entirety. 
    
    
     BACKGROUND 
     The present invention generally relates to power distribution networks and, in particular, a system and a method of efficiently detecting faults throughout the system using data analysis and probabilistic prediction techniques. 
     Power distribution networks often comprise many different nodes or points in the network and it is important to be able to detect when a node is powered and when a stretch of distribution line between nodes is faulted. Because the sensors used to detect the powered state at the many locations throughout the network may be noisy, it is preferable to view the state of the network at each point probabilistically. Furthermore, because of the scale of the network, computing the marginal probability density functions at each location in the graph is most often computationally impractical. 
     SUMMARY 
     In one form, a system for detecting faults in a power distribution network is described. The system includes data input devices coupled to the power distribution network, the data input devices receiving data from the power distribution network. A memory device is coupled to the one or more data input devices comprising a data management module that stores the received data from the one or more data input devices. A processing device comprising a processor is coupled to the memory device. The processing device comprising processor executable instructions for determining, based on the received data, a first estimated probability that each node of a plurality of nodes of the power distribution network is powered and determining, and a second estimated probability that each distribution line of a plurality of distribution lines connecting the plurality of nodes is faulted. The processing device further comprising processor executable instructions for assigning a visual characteristic to each node of the plurality of nodes based on the first estimated probability for the corresponding node and to each distribution line of the plurality of distribution lines based on the second estimated probability for the corresponding distribution line. 
     In another form, a method for detecting faults in a power distribution network is described. A signal is transmitted to a plurality of nodes in the power distribution network and received by the plurality of nodes in the power distribution network. One or more nodes of the plurality of nodes are monitored to determine a detection status of the transmitted signal. Using the detection status, a first estimated probability is calculated for each node in the power distribution network and a second estimated probability is calculated for each distribution line in the power distribution network. The first estimated probability indicating the likelihood that the nodes are powered and the second estimated probability indicating the likelihood that the distribution lines are not faulted. 
     Other objects and features will be in part apparent and in part pointed out hereinafter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS AND APPENDIX 
       FIG. I is a block diagram showing the power distribution network and the connected devices and systems for implementing the described invention. 
         FIG.  2    is a block diagram showing the modules making up the probabilistic fault detection application. 
         FIG.  3    is a diagram of nodes and edges representative of a power distribution network. 
         FIG.  4    is a diagram of message passing among the nodes representative of a power distribution network. 
     
    
    
     APPENDIX A illustrates the mathematical basis for the sum-product algorithm as described and it provides a pseudo code implementation of the algorithm. 
     Corresponding reference characters indicate corresponding parts throughout the drawings. 
     DETAILED DESCRIPTION 
     The described system connects to and receives data from a power distribution network. The system uses the received data to determine important information about the functionality of the network.  FIG.  1    shows a block diagram  100  of the power distribution network  102 , sensing devices  104 , a data management system  108 , a processing system  110 , and a user interface  114 . The processing system  110  includes a probabilistic fault detection (PFD) application  112 . In the diagram  100 , the sensing devices  104  are mechanically, electrically, and/or communicatively connected to aspects of the network  102 . As illustrated in  FIG.  1   , the sensing devices (i.e., sensors)  104  (e.g. TWACS data, RF System Data, etc.) can be part of the data management system  108 , although other configurations are also contemplated. The sensing devices  104  are also communicatively connected to the data management system  108 , which is in turn communicatively connected to the processing system  110 . The processing system is also electrically and/or communicatively connected to the user interface  114 . In one form, the sensing devices  104  collect data that are not statistically independent of the state of the power distribution network  102  and relay that data to the PFD application  112 . Thus, the sensing devices  104  need not be attached to the network in any way. 
     In an embodiment, the primary method of gathering data from the power distribution network  102  is through the use of sensing devices  104  connected to the network  102 . The sensing devices  104  may be connected to the network  102  at the transmission lines, the distribution lines, the power substations, the power meters, or any other point that makes up the power distribution network  102 . The system and method herein is described at the distribution level. However, the system and method herein can be generalized to transmission lines and, as used herein, transmission lines includes distribution lines. In an embodiment, the data collected by the sensing devices  104  comprises direct power data and data values which are indicative of the state of the network point to which the devices  104  are connected. In one embodiment, a sensing device (i.e., sensor)  104  is anything that provides data that is not statistically independent of the powered state of the network  102 . Sensing devices  104  need not be connected to the network  102 . As indicated in  FIG.  1   , customer calls and linemen observations are considered sensors for purposes of this system. Exemplary sensing devices  104  include, but are not limited to, those that detect electrical quantities, including voltage, electric current, electrical resistance, electrical conductance, electrical reactance, susceptance, magnetic flux, electrical power, inductance, capacitance, electrical impedance, electrical admittance, phase characteristics, frequency, gain, consumption, demand, time-of-use, reactive energy, power factor, Q-hour, apparent power, and the like. Exemplary sensing devices  104  that are indicative of the state of the network include customer calls, communication link data, thermal imagery of distribution lines, transient detectors, and the like. The sensing devices  104  are adapted to transmit data via a communication network. The communication network may be any network that facilitates the exchange of data, such as those that operate according to the IEEE 802.3 protocol (e.g., Ethernet), the IEEE 802.11 protocol (e.g., Wi-Fi), or the IEEE 802.16 protocol (e.g. WiMAX), for example. In another embodiment, the communication network is any medium that facilitates the physical transfer of data through serial or parallel communication channels (e.g., copper, wire, optical fiber, computer bus, cellular/wireless communication channel, radio frequency network, etc.). 
     According to one aspect, the data collected from the sensor  104  can include very low frequency (VLF) sync detection data, interactive voice response (IVR) data, lineman observation data, magnetic induction data, Supervisory Control and Data Acquisition (SCADA) data, infrared imagery data, and other data. 
     VLF sync detection data corresponds to responses that are detected or measured at targeted sites within the power distribution network  102  after one or more VLF band signals have been injected into the network  102 . The process of detecting responses after injecting VLF band signals is described below in more detail. 
     1VR data includes data collected via an automated telephony system that interacts with callers to receive or gather information and/or route calls to an appropriate recipient. IVR data can be received through voice telephone input and/or touch-tone keypad selections. 
     For example, a lineman may use a data input device to record and communicate information about characteristics or properties of network  102 . 
     Magnetic induction data includes data collected via a magnetic field sensing device. For example, the magnetic field sensing device may be a portable device that generates a measurable current when placed or positioned within a certain distance of an active or live power line. 
     Infrared (IR) imagery data includes data collected using an infrared sensing device. For example, the infrared sensing device senses whether the temperature of a particular component or equipment (e.g., switches, connectors, etc.) within the network  102  has exceeded a threshold temperature and, thus, is indicative of component or equipment failure. In one embodiment, the infrared sensing device measures the temperature of the conduction material to determine the current flowing through it. 
     Supervisory Control and Data Acquisition (SCADA) data corresponds to data acquired or collected through a SCADA system. 
     According to another aspect, the power distribution network  102  and/or the sensing devices  104  are components of an advanced metering infrastructure (AMI) system and the data management systems  108  collects and stores AMI data. The AMI data stored by the data management system includes, for example, Two-Way Automated Communication System (TWACS) data, Radio Frequency (RF) system data, and/or cellular system data. 
     TWACS data includes in-bound and/or out-bound messages that are communicated through power lines between substation communication equipment and remote communication equipment, such as a meter at a customer site. Outbound messages typically are used to check on the status of the power usage at the customer site (polling), convey instructions related to power usage at the site, etc. Inbound messages provide information or data about power usage at the customer site. 
     RF system data includes message data transmitted over a radio frequency between components of a smart power distribution network. For example, meters located at customer sites are equipped with a communication module that enables each meter to generate and transmit data via a RF communication signal to one or more components of the smart power distribution network. 
     Cellular system data includes data communicated between components of a smart power distribution network through a cellular communication network. For example, meters located at customer sites are equipped with a communication module that enables each meter to communicate data to one or more components of the smart power distribution network through a cellular communication infrastructure. 
     The processing system  110  connects to the data management system  108 . For example, processing system  110  may be electrically connected to data management system  108  or communicatively connected via a communication network, such as those further described herein. The processing system  110  receives the data from the data management system  108  and processes it into useful information, which is then used to make decisions about the operation of the power distribution network  102 . In an embodiment, the processing system  110  comprises a probabilistic fault detection application  112  stored on computer readable media for determining the probability that points within the network  102  are powered or faulted, as further described herein. For example, the probabilistic fault detection application  112  may be embodied as processor-executable instructions on the computer readable medium and execution of the instructions by a processing device of the processing system  110  provides a determination of the probability that points within the network  102  are powered or faulted. In an embodiment, the processing system  110  is further connected to a user interface  114  which enables users to view the results of the data processing of the processing system  110  via a display device and to interact with the system as a whole in response to the data processing. The processing system  110  and the user interface  114  may be embodied on the same computing device or on separate computing devices. 
     The probabilistic fault detection (PFD) application  112  is a vital part of the described invention.  FIG.  2    is a block diagram  200  representative of an exemplary PFD application  202  and the components thereof. In an embodiment, the PFD application  202  comprises a query module  204 , a fault probability estimation module  206 , a reporting module  208 , and a feedback module  210 . The four modules work in conjunction to interpret data gathered from the sensors  104  to provide information about the state of various points of the network. The query module  204  connects to the data management system  108  and is adapted to query for data and retrieve the data for use by the fault probability estimation module  206 . The query module  204  may query the data management system  108  at regular intervals to ensure the most current data is being used. In an embodiment, query module  204  comprises processor executable instructions embodied on the computer readable medium of processing system  110  to provide the query module  204  via a software environment. In an embodiment, query module  204  is adapted to retrieve data from data management system  108  by transmitting a query to data management system  108 , which data management system  108  receives and uses to select stored data that satisfies the query. The data management system  108  then transmits the selected data to probability estimation module  206 . 
     The probability estimation module  206  receives the data provided by the query module  204  and analyzes the data to generate probabilistic information about the state of points within the power distribution network  102 . In an embodiment, the probability estimation module  206  executes an algorithm on the data in order to generate the results (e.g., probabilistic information). This algorithm is described further below and in Appendix A. This algorithm corresponds to a means for estimating fault probability, according to some embodiments. In an embodiment, probability estimation module  206  comprises processor-executable instructions embodied on the computer readable medium of processing system  110  to provide the probability estimation module  206  via a software environment. 
     The reporting module  208  takes the generated probabilistic information from the probability estimation module  206  and generates reports containing the information. The reports are generated in such a way as to provide the probabilistic information to users of the system in a useful and understandable way when displayed via a display device. In an embodiment, reporting module  208  comprises processor-executable instructions embodied on the computer readable medium of processing system  110  to provide the reporting module  208  via a software environment. 
     The feedback module  210  provides feedback to the data collection mechanisms (e.g., sensing devices  104 ) regarding nodes that should be targeted for data collection. In an embodiment, feedback module  210  uses computed probabilistic information to guide the data collection process for points within the power distribution network  102 . For example, computed probabilities between very near 0 and very near lare reflective of certainty in the state of the network  102 . Those nodes or groups of nodes with uncertain states (i.e. those nodes with computed probabilities not near 0 or 1) are targeted for data collection. In an embodiment the probability that a given node is targeted for data collection is proportional to the entropy of the computed probability of its state. This forms a feedback loop from the probability computation to the data management system  108 . Among the advantages of this approach are that the computed probabilities detect regions of the network  102  for which the presently available data are insufficient. In an embodiment, feedback module  210  comprises processor-executable instructions embodied on the computer readable medium of processing system  110  to provide the feedback module  210  via a software environment. 
     According to another aspect, feedback module  210  generates and sends commands to the sensing devices  104  to collect data from one or more nodes neighboring a particular node with uncertain states. For example, if computed probabilities associated with a particular node are indicative of an uncertain state with respect to the status of that node, the feedback module  210  may command one or more data sensing devices to compute probabilistic information about nodes that are adjacent to and/or neighbor that particular node. 
     According to another aspect, the feedback module  210  associates or assigns a visual characteristic to elements of a distribution map (e.g., see  FIG.  3   ) being displayed, for example, via the user interface  114 . The visual characteristic may be a color, a symbol, text, or some other indicator that conveys the state of a particular node or edge within the power distribution network. For example, if the visual characteristic is a color, red may indicate a fault state, green may indicate an active or powered state, and yellow may indicate an uncertain state. In an embodiment, the feedback module  210  associates or assigns a first type of visual characteristic (e.g., color) to nodes and a second type of visual characteristic (e.g., text) to transmission lines. In another embodiment, the feedback module  210  associates or assigns a common visual characteristic to nodes and transmission lines (e.g., color for both, text for both, etc.). 
       FIG.  3    shows a diagram of an exemplary power distribution network map  300  with nodes or vertices  302 ,  304 , and  308  representing any electrical devices, branches, or other points on the network  102  and edges  306  representing the distribution lines connecting those points. In an embodiment, this representation is used by the PFD application  202  to determine probability states for each node  302 ,  304 , and  308  and each edge  306 . The diagram shows a node  302  at the center connected to adjacent nodes  304  by edges  306 . The adjacent nodes have additional edges  306  that may connect to additional nodes not shown. It is important to be able to determine if the nodes of the network are powered and whether the distribution lines of the network are faulted or not. 
     In an embodiment, a power distribution network has at least one node which is a source node  308 , which may be representative of a power substation, generator, or the like. The source node  308  is a node which provides power from outside the network to the nodes of the network via the distribution lines  306 . A particular node  302  can only be powered if the distribution lines  306  between the source node  308  and the particular node  302  are not faulted and if the nodes between the source node  308  and the particular node  302  are powered. If the particular node  302  and the source node  308  are adjacent in the network and connected only by a single distribution line  306 , then the distribution line  306  between them must not be faulted for node  302  to be powered. Depending on the complexity of the network, a node may be powered from the source node along more than one path through the network. 
     Data is gathered from the power distribution network which is correlated with power loss at nodes in the network. In an embodiment, the data gathered is stochastic in nature in that the data is randomly determined, having a random probability distribution or pattern that can be analyzed statistically but may not be predicted precisely. In one form, the data is correlated with power loss at nodes in a probabilistic way, not a purely deterministic way. The gathered data may be in the form of signals passed between nodes throughout the power distribution network. In an embodiment, the power distribution network comprises an active sounding system as described in U.S. Patent Application No. 62/057,300. A very low frequency (VLF) band signal is transmitted throughout the network and received at each node in the network in varying patterns. In an embodiment, the signal is a VLF-band spread spectrum signal followed by a VLF-band communications message signal (“signal”) encoded with a unique identifier. When a concentrator device broadcasts a particular signal on the network, it also reports to a central processing station that it broadcast that particular signal with the unique identifier at that particular time. When a detector device detects the broadcasted signal, it decodes the signal and notifies the central processing station that it received a signal with the unique identifier. The central processing station can use a detection status of a transmitted signal to estimate the probability that each node in the network is powered and/or the probability that each stretch of distribution line is faulted. The detection status for a particular transmitted signal is, for example, a value, flag, or other attribute that indicates whether that particular transmitted signal was detected by the detection device. 
     According to one aspect, the central processing station can determine a detection rate for each detector device based on what percentage of broadcast signals they report receiving. The detection rate of the signal at each node constitutes a random process which contains information on the state of the network. In the event that a fault occurs in the network, the detection rate of the signal at affected nodes changes. The detection rate data gathered is used to estimate the probability that each node in the network is powered and the probability that each stretch of distribution line is faulted. In general, the algorithms described herein not necessarily need to compute a detection rate and wait for it to change. The relative merits of each hypothesis can re-evaluated with every detection and missed detection. In an embodiment, sensors may gather the data from the nodes of the network and/or from the distribution lines of the network. In another embodiment, the signal data as described above is passed throughout the network using TWACS communication technology. Other data sources may also be used, including SCADA data or even customer reporting data and manually entered data, so long as the data used are not statistically independent with the powered state of the network. 
     In an embodiment, the estimates of whether the nodes are powered or the distribution lines are faulted are calculated using the sum-product algorithm by the PFD application  202 . Alternatively, a max-product algorithm can be used. The difference between the two is that the sum-product algorithm can be used to compute the minimal mean square error (MN&#39;ISE) estimates of the state and the max-product is used to compute the maximum aposteriori (MAP) estimates. The data are the same for both approaches, as is the structure of the message passing algorithm. One difference is in the equations used to compute the messages at each iteration of the algorithm. The nodes of the network pass messages to the other adjacent nodes and edges in the network.  FIG.  4    shows a diagram of a group of nodes of a network  400  passing messages between each other. The message  408  passed from a first node  402  to a second node  404  comprises a combination of the information from the messages  410  sent to the first node  402  from all other adjacent nodes  406 , not including the second node  404 . Similarly, a message  412  passed from the second node  404  to the first node  402  comprises a combination of the information from the messages  416  sent to the second node  404  from all other adjacent nodes  414 , not including the first node  402 . In an embodiment, the messages being passed are probability density functions (PDFs) as described in Appendix A, including the Forney Factor graph as represented in  FIG.  1    on page 4 of Appendix A. For a node to be powered one of the adjacent nodes must be powered and the distribution line between the two nodes must be connected. The probability that a node is powered is a function of the probabilities that the adjacent nodes are powered and the connecting distribution lines are not faulted. The messages being passed contain information about the probability that the node receiving the signal is powered. In an embodiment, the edges between the nodes also pass messages that include information about the probability that the distribution lines represented by the edges are faulted. 
     The data gathered from the network does not perfectly represent the state of the nodes and distribution lines. The data from a node may indicate that it is not powered when it actually is powered, or the data from the node may indicate that it is powered when it is not actually powered. The same applies for data gathered about a distribution line. The probability that the data being gathered for each node and distribution line is accurate is further used within the sum-product algorithm calculation. 
     The data gathered from the network may be data that is only from some of the nodes or some of the edges. A message passing algorithm such as the sum-product algorithm is implemented by the PFD application  202  to extrapolate the probabilities that apply to the other points in the network. The sum-product algorithm is an example of one fast method for computing the marginal PDF from the joint PDF of many variables. In this case, those many variables are the states of every edge and node in the power distribution graph. In one form, a belief propagation algorithm can be employed, where a message being passed from a node  402  to a node  404  contains information indicating the “belief’ of the node  402  that the node  404  is powered based on the messages node  402  has received from other adjacent nodes  406  and any sort of functionality node  402  may itself provide. Belief propagation algorithms are a superset of the sum-product algorithm. They need not be rigorous. Many are heuristic. The sum-product algorithm is rigorous on graphs without loops and computes the marginal PDFs precisely. In an embodiment, aspects of the invention implement a sum-product algorithm to solve equations more quickly and efficiently than straightforward solutions. In another embodiment, aspects of the invention implement a sum-product algorithm to provide real-time updates of the faulted state of the network and alerts in less time than would otherwise be possible. The marginal PDF computed is conditioned on the detection status of every node in the network. The detection rate is a parameter of the PDF. In one embodiment, it is not estimated but considered it to be known. Other embodiments consider the detection rate an unknown nuisance parameter and estimate it by some method internal to the algorithm. See Appendix A. 
     In an embodiment, the data gathered is analyzed by a computer system executing the PFD application  202  and computing probabilities for each of the nodes and distribution lines, or edges, according to the sum-product or max-product algorithm calculation. The messages sent between the nodes and edges are dependent on other messages received. Due to this interdependence, the messages to send may be updated multiple times as the received messages also change. Eventually, the message values between the nodes and edges converge to stable values. In a Forney Factor graph (FFG) without cycles, the algorithm completes after a finite number of calculations, as noted in Appendix A. Once the algorithm completes or stabilizes, the message values between nodes and edges indicate the likelihood of a node in the network being powered or a distribution line in the network being connected. See Appendix A. for the algorithm pseudo code. In an embodiment, the algorithm allows aspects of the invention to fuse widely disparate sources of stochastic data and infer the state of the network. In another embodiment, the algorithm allows aspects of the invention to utilize numerous imprecise sensors to quickly deduce the probability that outages are occurring on the network and to locate the source of the problem causing the outages. 
     In FFGs&#39; with cycles, the calculating and updating of the messages for each node and edge includes several different steps. These steps comprise assigning initial values to each node and edge in the network and creating initial messages from each node and edge based on the initial values. The initial values are based on the data gathered from the node or edge if available. Otherwise, the initial value is set to a positive value between 0 and 1. Based on the initial values, the algorithm iterates through the nodes and edges repeatedly, updating the messages for each. 
     Some variant of an iterative approach should be used on FFGs with cycles. Once the updated outgoing message components are calculated for each node in the network, the new incoming messages for each node and edge are processed and incorporated into the outgoing messages of the nodes and edges. The algorithm repeats. The algorithm loops repeatedly and may eventually converge to stable values for the messages between the nodes and edges. This approach is not necessary on FFGs without cycles. 
     The stable values at each point of the network represent estimations of the state of that point in the actual power distribution network. The values are then interpreted by users or other computer programs in order to determine if action needs to be taken based on the estimations. Whether a value is considered high or low is dependent on the network and the decisions of the manager of the power distribution network. In an embodiment, an additional algorithm is used to convert the estimation values at each point in the network to binary values indicating whether a given point is faulted or not. 
     The Abstract and summary are provided to help the reader quickly ascertain the nature of the technical disclosure. They are submitted with the understanding that they will not be used to interpret or limit the scope or meaning of the claims. The summary is provided to introduce a selection of concepts in simplified form that are further described in the Detailed Description. The summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the claimed subject matter. 
     For purposes of illustration, programs and other executable program components, such as the operating system, are illustrated herein as discrete blocks. It is recognized, however, that such programs and components reside at various times in different storage components of a computing device, and are executed by a data processor(s) of the device. 
     Although described in connection with an exemplary computing system environment, embodiments of the aspects of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations. The computing system environment is not intended to suggest any limitation as to the scope of use or functionality of any aspect of the invention. Moreover, the computing system environment should not be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with aspects of the invention include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, mobile telephones, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. 
     Embodiments of the aspects of the invention may be described in the general context of data and/or processor-executable instructions, such as program modules, stored one or more tangible, non-transitory storage media and executed by one or more processors or other devices. Generally, program modules include, but are not limited to, routines, programs, objects, components, and data structures that perform particular tasks or implement particular abstract data types. Aspects of the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote storage media including memory storage devices. 
     In operation, processors, computers and/or servers may execute the processor executable instructions (e.g., software, firmware, and/or hardware) such as those illustrated herein to implement aspects of the invention. 
     Embodiments of the aspects of the invention may be implemented with processor executable instructions. The processor-executable instructions may be organized into one or more processor-executable components or modules on a tangible processor readable storage medium. Aspects of the invention may be implemented with any number and organization of such components or modules. For example, aspects of the invention are not limited to the specific processor-executable instructions or the specific components or modules illustrated in the figures and described herein. Other embodiments of the aspects of the invention may include different processor-executable instructions or components having more or less functionality than illustrated and described herein. 
     The order of execution or performance of the operations in embodiments of the aspects of the invention illustrated and described herein is not essential, unless otherwise specified. That is, the operations may be performed in any order, unless otherwise specified, and embodiments of the aspects of the invention may include additional or fewer operations than those disclosed herein. For example, it is contemplated that executing or performing a particular operation before, contemporaneously with, or after another operation is within the scope of aspects of the invention. 
     When introducing elements of aspects of the invention or the embodiments thereof, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. 
     In view of the above, it will be seen that several advantages of the aspects of the invention are achieved and other advantageous results attained. 
     Not all of the depicted components illustrated or described may be required. In addition, some implementations and embodiments may include additional components. Variations in the arrangement and type of the components may be made without departing from the spirit or scope of the claims as set forth herein. Additional, different or fewer components may be provided and components may be combined. Alternatively or in addition, a component may be implemented by several components. 
     The above description illustrates the aspects of the invention by way of example and not by way of limitation. This description enables one skilled in the art to make and use the aspects of the invention, and describes several embodiments, adaptations, variations, alternatives and uses of the aspects of the invention, including what is presently believed to be the best mode of carrying out the aspects of the invention. Additionally, it is to be understood that the aspects of the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The aspects of the invention are capable of other embodiments and of being practiced or carried out in various ways. Also, it will be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.