Patent Publication Number: US-7720620-B2

Title: System and method for determining harmonic contributions from non-linear loads without disconnecting any load

Description:
CROSS REFERENCE TO RELATED APPLICATION 
   This application is a continuation-in-pad of U.S. utility application entitled “System and Method for Determining Harmonic Contributions from Non-Linear Load,” assigned Ser. No. 10/940,101, filed Sep. 14, 2004, now issued as U.S. Pat. No. 7,013,227, which claimed the benefit of U.S. provisional application entitled, “Method to Discriminate Between the Contributions of the Customer and the Power System to the Harmonic Disturbance,” assigned Ser. No. 60/503,003, filed Sep. 15, 2003, both of which are entirely incorporated herein by reference. 

   TECHNICAL FIELD 
   Various embodiments are generally related to electric power supply systems and, more particularly, are related to systems and methods for identifying power system harmonics using an artificial neural network. 
   BACKGROUND 
   In electrical power supply networks, harmonics is a term used to describe the shape or characteristic of a distorted non-sinusoidal voltage or current waveform with respect to the fundamental frequency sine wave. Harmonic currents generated by non-linear loads, such as power electronic equipment, arc furnaces, saturating inductances, and other types of solid-state load devices, are injected into an electric power supply network. Such injected harmonic currents distort the supply source in an undesirable manner. These harmonic currents are sometimes referred to as contributions from the customer. 
   Harmonic currents cause harmonic voltage drops when harmonic currents generated by the customer&#39;s non-linear loads are injected into the electric power supply network. Accordingly, the supply voltage at the customer is no longer sinusoidal. Harmonic currents that distort the sinusoidal supply voltage (and/or supply current) in an electric power supply network give rise to several problems, such as: creating distorted supply voltages to loads, which in some cases cause overheating of customer equipment, or additional losses and overheating of network equipment, creating additional losses in transformers and/or cables, inducing electromagnetic interference onto neighboring telecommunication circuits, creating light flicker, causing malfunctioning of metering, current and/or voltage transducers, and/or causing malfunctioning of protection systems. 
   The resultant distorted load current, when measured or metered at the connection point of the customer to the electric power supply network, consists of two components; that due to contributions from the customer&#39;s non-linear load, and that due to contributions from the power system. Accordingly, it is desirable to distinguish between these two components, particularly without disconnecting the customer from the network to perform conventional load testing or analysis. 
   Other customers may be connected to the same connection point, or relatively close to the connection point, of the customer having loads that generate undesirable harmonic currents. Since these harmonic currents are injected into the electric power supply network, the supply voltage (and/or current) provided to the other customers may be distorted by the customer having the non-linear loads (that do cause harmonics). Accordingly, these other customers may not receive an acceptable level of power quality from the electric power supply network, thereby degrading performance of their load devices. 
   These other customers may have linear loads (that do not cause harmonics) and/or non-linear loads (that do cause harmonics). In some situations, the non-linear loads of the other customer, by themselves, would not otherwise cause significant unacceptable distortions in the supply voltage. However, the distortions from both customers may have a cumulative effect on the total harmonic distortion in the supply voltage. Accordingly, it is desirable to distinguish between harmonic distortion contributions from a plurality of customers supplied from the electric power supply network. 
   Furthermore, traditional testing methods provide results that are problematic at best. That is, since the non-linear loads may be on or off at the time of testing, it is problematic whether or not testing results accurately reflect harmonics of interest. For example, if the test is conducted during the day, harmonics induced by solid-state lighting ballasts may or may not be on at the time of testing. In a large plant, determining when a particular solid-state controlled motor is operating requires coordination. If many solid-state controlled motors are in the plant, coordination becomes even more difficult. Furthermore, even if all of the solid-state controlled motors could be simultaneously operating during the test, or if testing results of individual motors could be computationally aggregated, is the resultant harmonic contribution from all of the solid-state controlled motors a reasonable representation of actual operating conditions of the plant? Accordingly, it is desirable to be able to test for harmonic contributions on an ongoing basis, and to differentiate harmonic contributions from individual customers when multiple customers are connected at a common connection point (or relatively close together). 
   SUMMARY 
   A system and method for determining harmonics caused by non-linear loads are disclosed. Briefly described, one embodiment is a method comprising metering voltage on an electric power system; metering current on the electric power system; determining a predicted current based upon the metered voltage; comparing the predicted current with the metered current; and determining a harmonic current component using a plurality of weights determined when the predicted current converges with the metered current. 
   Another embodiment is a system comprising an artificial neural network configured to receive information corresponding to metered voltage, to train itself such that a predicted current is determined which converges with a metered current, and to determine a harmonic current component using a plurality of weights determined when the predicted current converges with the metered current; and a processor configured to execute the artificial neural network. 
   Another embodiment is an artificial neural network that determines harmonics caused by non-linear loads, comprising a training artificial neural network that determines a predicted current from a metered voltage, and that determines a plurality of weights when the predicted current converges with a metered current; and an estimating artificial neural network that determines a harmonic current component using the determined weights received from the training artificial neural network. 
   Another embodiment is a method comprising metering voltage on an electric power system; metering current on the electric power system; determining a predicted voltage based upon the metered current; comparing the predicted voltage with the metered voltage; and determining a harmonic voltage component using a plurality of weights determined when the predicted voltage converges with the metered voltage. 
   Another embodiment is a system comprising an artificial neural network configured to receive information corresponding to metered current, to train itself such that a predicted voltage is determined which converges with a metered voltage, and to determine a harmonic voltage component using a plurality of weights determined when the predicted voltage converges with the metered voltage; and a processor configured to execute the artificial neural network. 
   Another embodiment is an artificial neural network that determines harmonics caused by non-linear loads, comprising a training artificial neural network that determines a predicted voltage from a metered current, and that determines a plurality of weights when the predicted voltage converges with a metered voltage; and an estimating artificial neural network that determines a harmonic voltage component using the determined weights received from the training artificial neural network. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The components in the drawings are not necessarily to scale relative to each other. Like reference numerals designate corresponding parts throughout the several views. 
       FIG. 1  is a block diagram illustrating an embodiment of the artificial neural network (ANN) non-linear load analyzer coupled to a simplified single-line diagram of an electric power supply network. 
       FIG. 2  is a block diagram illustrating in greater detail an embodiment of the ANN non-linear load analyzer coupled to the simplified single-line diagram of the electric power supply network. 
       FIG. 3  is a conceptual block diagram illustrating an embodiment of the ANN non-linear load analyzer. 
       FIG. 4  is a block diagram illustrating in greater detail an exemplary embodiment of an ANN non-linear load analyzer. 
       FIG. 5  is a flowchart illustrating an embodiment of a process for determining harmonic components using an embodiment of the ANN non-linear load analyzer. 
       FIG. 6  is a block diagram illustrating an embodiment of an ANN source model identifier coupled to a simplified single line diagram of a source equivalent circuit. 
       FIG. 7  is a block diagram illustrating in greater detail an embodiment of the ANN source model identifier coupled to the simplified single line diagram of the source equivalent circuit. 
       FIG. 8  is a conceptual block diagram illustrating an embodiment of the ANN source model identifier. 
       FIG. 9  is a block diagram illustrating in greater detail an exemplary embodiment of an ANN model source identifier. 
       FIG. 10  is a flow chart illustrating an embodiment of a process for determining harmonic components using an embodiment of the ANN source model identifier. 
   

   DETAILED DESCRIPTION 
   The artificial neural network (ANN) non-linear load analyzer  100  provides a system and method for determining harmonic contributions from non-linear loads.  FIG. 1  is a block diagram illustrating an embodiment of the ANN non-linear load analyzer  100  coupled to a simplified single-line diagram of a portion an electric power supply service system  102 . Service system  102  is ultimately coupled to, and power supplied from, the electric power supply system  104 . Service system  102  may represent either a single-phase or a three-phase system. Furthermore, the operating voltage of service system  102  may range from customer service voltage, to a distribution voltage, up through EHV (extra high voltage). Also, service system  102  may be radial or part of a network. 
   Typically, electric power supply network  104  provides service to customers via a conductor  106 . Often, the service provider provides service to multiple customers over a common component, illustrated here as conductor  106 . In some cases, a tap  108  on conductor  106  provides coupling to a second conductor  110 , which then extends to the other customer(s). Or, the customer facilities  112  of interest, having the linear load  114  and the non-linear load  116 , may tap off from the conductor  106  at the connection interface  118 . Here, conductor  120  is illustrated as providing service from the connection interface  118  to the customer facilities  112 . 
   It is appreciated that the above-described service system  102  is a very simplified diagram of an exemplary service system. Other service systems may provide service using transformers (not shown), or provide service directly from a substation bus bar (not shown). Service voltages may vary from one service system to another, or may be of the same voltage. Also, voltages along a service system may vary if transformers are employed to alter voltages. As noted above, service system  102  may represent either a single-phase and/or a three-phase system. For example, but not limited to, conductor  106  could be a three-phase conductor network and the service provided to the customer facilities  112  could be single-phase. 
   Many service variations are possible which may be analyzed for harmonics by embodiments of the ANN non-linear load analyzer  100 . Accordingly, the ANN non-linear load analyzer  100  may be configured to couple to single-phase, two phase and/or three-phase systems; may be configured to couple to various system voltages; and may even be configured to couple to power supply systems of different operating frequencies (such as, but not limited to, the 60 hertz system commonly employed in the United States, or the 50 hertz system commonly employed in European systems). 
   Embodiments of the ANN non-linear load analyzer  100  are configured to conveniently couple to a service system  102 , and/or the customer facilities  112 , such that data may be collected to determine harmonic components caused by a customer&#39;s non-linear load  116 . In  FIG. 1 , a simplified connector  122  illustrates the ANN non-linear load analyzer  100  coupled to the connection interface  118  that provides service to the customer tapped off of conductor  106  in this simplified example. Greater detail of the various connection aspects of the ANN non-linear load analyzer  100  are provided below. However, it is appreciated that the ANN non-linear load analyzer  100  could couple to an electric power system at any suitable point, even directly to the customer&#39;s non-linear load  116 . 
   It is appreciated that the ANN non-linear load analyzer  100  determines harmonic contributions from non-linear loads downstream from the connection interface  118 . More particularly, the ANN non-linear load analyzer  100  determines harmonic contributions from non-linear loads downstream from the current transformer  204  ( FIG. 2 ) which provides the metered current information, described in greater detail hereinbelow. Accordingly, depending upon how the ANN non-linear load analyzer  100  is connected to the service system  102 , loading from one or more customers may be aggregated. For example, in a situation where a customer is billed for harmonic components that the customer injects into the service system  102 , the connection interface  118  may be configured to provide metered voltage and current applicable to that specific customer. In another situation, the ANN non-linear load analyzer  100  might be coupled to a convenient location on service system  102 , such as at a distribution substation, which serves more than one customer. 
     FIG. 2  is a block diagram illustrating in greater detail an embodiment of the ANN non-linear load analyzer  100  coupled to the simplified single-line diagram of the electric power supply network  104 . The ANN non-linear load analyzer  100  includes an artificial neural network (ANN)  200  and summing circuit  202 . 
   A metering potential transformer (PT) and a metering current transformer (CT)  204  are illustrated as residing at the connection interface  118 . The PT provides a voltage transformation such that voltage of conductor  106  may be sensed and communicated to voltage transducer  206 . The CT provides a current transformation such that current of connection  106  may be sensed and communicated to current transducer  208 . Other voltage and/or current sensing devices may alternatively meter voltage and current, respectively. 
   Voltage transducer  206  converts the sensed alternating current (AC) voltage into a signal, such as a digitally sampled signal, that is proportional to the sensed AC voltage from the PT. Sensed voltages may be phase-to-phase voltages or phase-to-neutral voltages. Current transducer  208  converts the sensed AC current into a signal, such as a digitally sampled signal, that is proportional to the sensed AC current from the CT. 
   Typically, PTs and CTs are available at various points along the electric power system when various other functions associated with operation of the electric power system are performed. For example, a metering PT and a metering CT may be provided for revenue billing. Or, a PT and/or CT may be for protective devices, auxiliary power and/or communication devices. When such PTs and/or CTs  204  are readily available, the associated voltage transducer  206  and/or current transducer  208  may be coupled to those devices. Thus, information corresponding to the voltage and information corresponding to the current may be available from already-available devices for some embodiments of the ANN non-linear load analyzer  100 . In other situations PTs, CTs, voltage transducers  206  and/or the current transducers  208  may not be available. Accordingly, other embodiments of the ANN non-linear load analyzer  100  may be equipped with such devices, or such devices may be separately installed for the ANN non-linear load analyzer  100 . 
   As illustrated in  FIG. 2 , the ANN  200  receives a signal corresponding to metered voltage, via connection  210 . The ANN  200  is configured to output a predicted current î, via the logical connection  212  (since the predicted current î is computationally determined by executing software, the ANN logic  408  illustrated in  FIG. 4 , connection  212  is not a physical connection). The predicted current î is input into the summing circuit  202  such that the predicted current î is summed with the signal corresponding to metered current, provided on connection  214 , such that a difference between the predicted current î and the metered current is determined. The output of the summing circuit  202  (the difference between the predicted current î and the metered current) is the current error signal, e, output on the logical connection  216 . The current error signal e is returned to the ANN  200 . 
   As the current error signal e approaches zero, or converges to within a predefined threshold, it is appreciated that the ANN  200  has completed training with respect to predicting current. That is, the ANN  200  is accurately predicting current metered at connection interface  118 , within some predefined threshold. 
   When predicted current î is accurately predicted, the ANN  200  may then computationally determine the harmonic current component, î—distorted. Information corresponding to the harmonic current component, î—distorted, is output on connection  218 , and may be used for a variety of purposes, as is well understood in the arts. 
     FIG. 3  is a conceptual block diagram illustrating an embodiment of the ANN non-linear load analyzer  100 . The ANN  200  (see also  FIG. 2 ), in this embodiment, includes two artificial neural networks, the training ANN  302  and the estimating ANN  304 . It is appreciated that the training ANN  302 , the estimating ANN  304  and the summing circuit  202  may be implemented as software, firmware, or combinations of software and firmware. 
   In this conceptual embodiment, the training ANN  302  receives the signal corresponding to metered voltage, via connection  210 . At the end of a training time, or sampling interval, the training ANN  302  outputs the predicted current î, via connection  212 , to the summing circuit  202 . Metered current is then summed with the predicted current î (such that a difference between the predicted current î and the metered current is determined). In one embodiment, the metered current at the end of the sampling interval is used for the comparison. (In other embodiments, the metered current used for training may be used when this information is stored for later retrieval.) 
   The current error signal e is returned on connection  216  to the training ANN  302 . As noted above, when the current error signal e approaches zero, or converges, the training ANN  302  has completed training with respect to predicting current. In some embodiments, the current error signal e has converged when the difference between the predicted current î and the metered current is less than and/or equal to a predefined threshold. 
   The metered current may be considered as a pure sinusoidal wave provided by the electric power supply network  104  with harmonic components injected thereon by the customer non-linear load  116  ( FIGS. 1 and 2 ). Thus, the corresponding predicted current î is a computed sinusoidal wave current distorted by the harmonics. The computed predicted current î may be represented by any suitable mathematical equation or algorithm, which is selected based upon the design of the particular embodiment of the ANN non-linear load analyzer  100 . 
   In one embodiment, the load admittance is calculated. Accordingly, the load admittance is modeled by a suitable equation with determined weights. In another embodiment, weights associated with the equation defining the predicted current î are determined. After the weights have been determined by the training ANN  302 , the weights are communicated to the estimating ANN  304  (represented conceptually by the directional arrow  306 ). 
   While the estimating ANN  304  determines the harmonic current component (î—distorted), the training ANN  302  in one embodiment continues sampling. Weights are adjusted at each sample depending on the degree of convergence (the difference between the predicted current î and the metered current). When the estimating ANN  304  has determined the harmonic current component (î—distorted), the estimating ANN  304  receives another set of weights from the training ANN  302 . In one embodiment, the received second set of weights are the most current value of the weights determined by the training ANN  302 . Then, the estimating ANN  304  determines another harmonic current component based upon the new weights. The above-described process may be continuously repeated. 
   Depending upon the embodiment, the training ANN  304  may then restart the training process to predict another predicted current î. Thus, after some period of time, weights associated with the subsequent predicted current î are determined. The newly determined weights may or may not equal the previously determined weights, depending upon whether the nature of the customer&#39;s non-linear load  116  and/or the power supply have changed by the time the second training period has been completed. That is, if the degree of convergence has changed since the previous sampling period, newly computed weights will be different. For example, a customer may have turned on (or off) a solid-state load, thereby changing the nature of the customer&#39;s non-linear load  116 , thereby resulting in a change in the degree of convergence. Accordingly, the training and determining harmonic components may be performed on a continual basis. 
   The estimating ANN  304  receives the determined weights (for the load admittance and/or predicted current î equation). The estimating ANN  304  then computationally applies the weights to a pure sinusoidal wave  308 . Distortion in the output of the sinusoidal wave (distorted by the determined weights) is then used to determine the harmonic current component (î—distorted) associated with the customer non-linear load  116 . 
   Information corresponding to the harmonic current component (î—distorted) and/or a determined load admittance associated with the customer non-linear load  116  may then be communicated onto connection  218 , and may be used for a variety of purposes, as is well understood in the arts. 
     FIG. 4  is a block diagram illustrating in greater detail an exemplary embodiment of an ANN non-linear load analyzer  100 . The exemplary embodiment comprises a processing system  402  and summing circuit  202 . Processing system  402  includes a processor  404  and memory  406 . In one embodiment, memory  406  includes regions for storing the summing circuit  202  (implemented as logic), artificial neural network (ANN) logic  408 , the above-described weights  410  and admittance  412 , data corresponding to the current error signal e  414 , the predicted current î  416 , and/or the harmonic current component (î—distorted)  418 , depending upon the particular embodiment. The information is saved into memory  406  for ongoing processing, and/or may be saved for later processing and/or analysis. In other embodiments, the information may be saved in other memories as needed, such as buffers or random access memories. 
   Also included are suitable interfaces that provide coupling to the above-described connections which communicate the various information. The metered voltage interface  420  provides coupling to connection  210  such that information corresponding to the metered voltage is received. The metered current interface  422  provides coupling to connection  214  such that information corresponding to the metered current is received. The output device interface  424  provides coupling to connection  218  such that information corresponding to the harmonic current component (î—distorted) is transmitted to a suitable device. 
   The above described components are communicatively coupled to each other via bus  426  and connections  428 . In alternative embodiments of processing system  402 , the above-described components are connectivley coupled to processor  404  in a different manner than illustrated in  FIG. 4 . For example, one or more of the above-described components may be directly coupled to processor  404  or may be coupled to processor  404  via intermediary components (not shown). 
   The above-described interfaces  420 ,  422  and/or  424  may be passive devices, or they may be active devices that process the received/transmitted signals into a suitable format. For example, the metered voltage interface  420  may be a simple coupling to connection  210  when the voltage transducer  206  ( FIG. 2 ) provides a suitable metered voltage signal that may be directly communicated onto bus  428  such that information corresponding to metered voltage is received by processor  404 . In another embodiment, the metered voltage interface  420  processes the metered voltage information received on connection  210  into a suitable signal that is directly communicated onto bus  428 . As another example, the output device interface  426  may communicate information corresponding to the harmonic current component (î—distorted) to a display such that a voltage or current signal is displayed to a viewer. Or, output device interface  426  may communicate information corresponding to the harmonic current component (î—distorted) to another processing system or memory for future analysis. 
   In various embodiments, the summing circuit  202  could be implemented as software (with its own processor and memory, not shown), as firmware, or as a combination of firmware and software. In another embodiment, an interface (not shown) to receive the metered current information on connection  214  is included such that the processor  404  computationally determines the current error signal e. In this embodiment, the interfaces  422  and  424  are omitted. 
     FIG. 5  shows a flow chart  500  illustrating a process used by an embodiment of ANN non-linear load analyzer  100  ( FIGS. 1-4 ). The flow chart  500  of  FIG. 5  shows the architecture, functionality, and operation of an embodiment for implementing the ANN logic  408  ( FIG. 4 ). An alternative embodiment implements the logic of flow chart  500  with hardware configured as a state machine. In this regard, each block may represent a module, segment or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in alternative embodiments, the functions noted in the blocks may occur out of the order noted in  FIG. 5 , or may include additional functions. For example, two blocks shown in succession in  FIG. 5  may in fact be substantially executed concurrently, the blocks may sometimes be executed in the reverse order, or some of the blocks may not be executed in all instances, depending upon the functionality involved, as will be further clarified hereinbelow. All such modifications and variations are intended to be included herein within the scope of this disclosure. 
   The process begins at block  502 . At block  504 , voltage on an electric power system is metered. At block  506 , current on the electric power system is metered. At block  508 , a predicted current based upon the metered voltage is determined. At block  510 , the predicted current is compared with the metered current. At block  512 , a harmonic current component is determined using at least one weight determined when the predicted current converges with the metered current. The process ends at block  514 . 
   An alternative embodiment of the ANN non-linear load analyzer  100  may be configured as an ANN source model identifier  600 .  FIG. 6  is a block diagram illustrating an embodiment of the ANN source model identifier  600  coupled to a simplified portion of an electric power supply service system  602 . Service system  602  is ultimately coupled to a source equivalent circuit  604 . Source equivalent circuit  604  may comprise an electric power supply network together with multiple loads (which could be linear or non-linear). Service system  602  may represent either a single-phase or a three-phase system. Furthermore, the operating voltage of service system  602  may range from customer service voltage, to a distribution voltage, up through EHV. Also, service system  602  may be radial or part of a network. 
   Typically, service is provided from an electric power supply network, as part of the source equivalent circuit  604 , through a conductor  606  to a non-linear load  616 . Often, service is provided to multiple customers over a common component, illustrated here as conductor  606 . A non-linear load  616  may tap off from the conductor  606  at the connection interface  618 . Here, conductor  620  is illustrated as providing service from the connection interface  618  to non-linear load  616 . 
   It is appreciated that the above-described service system  602  is a very simplified diagram of an exemplary service system. Other service systems may provide service using transformers (not shown), or provide service directly from a substation bus bar (not shown). Service voltages may vary from one service system to another, or may be of the same voltage. Also, voltages along a service system may vary if transformers are employed to alter voltages. As noted previously, service system  602  may represent either a single-phase and/or a three-phase system. As a non-limiting example, conductor  606  could be a three-phase conductor network and the service provided to the non-linear load  616  could be single-phase. 
   Many service variations are possible which may be analyzed for harmonics by embodiments of the ANN source model identifier  600 . Accordingly, the ANN source model identifier  600  may be configured to couple to single-phase, two-phase and/or three-phase systems; may be configured to couple to various system voltages; and may even be configured to couple to power supply systems of different operating frequencies (such as, but not limited to, the 60 hertz system commonly employed in the United States, or the 50 hertz system commonly employed in European systems). 
   Embodiments of the ANN source model identifier  600  are configured to conveniently couple to a service system  602 , and/or the non-linear load  616 , such that data may be collected to determine harmonic components caused by the non-linear load  616 . In  FIG. 6 , a simplified connector  622  illustrates the ANN source model identifier  600  coupled to the connection interface  618  that provides service to the non-linear load  616  in this simplified example. Greater detail of the various connection aspects of the ANN source model identifier  600  are provided below. However, it is appreciated that the ANN source model identifier  600  could couple to an electric power system at any suitable point. 
   It is appreciated that the ANN source model identifier  600  determines harmonic contributions from non-linear loads downstream from the connection interface  618 . More particularly, the ANN source model identifier  600  determines harmonic contributions from non-linear loads downstream from the potential transformer and current transformer  704  ( FIG. 7 ) which provides the metered voltage information, described in greater detail hereinbelow. Accordingly, depending upon how the ANN source model identifier  600  is connected to the service system  602 , loading from one or more customers may be aggregated. For example, in a situation where a customer is billed for harmonic components that the customer injects into the service system  602 , the connection interface  618  may be configured to provide metered voltage and current applicable to that specific customer. In another situation, the ANN source model identifier  600  might be coupled to a convenient location on service system  602 , such as at a distribution substation, which serves more than one customer. 
     FIG. 7  is a block diagram illustrating in greater detail an embodiment of the ANN source model identifier  600  coupled to the simplified single-line diagram of the source equivalent circuit  604 . The ANN source model identifier  600  includes an artificial neural network (ANN)  700  and summing circuit  702 . 
   A metering potential transformer (PT) and metering current transformer (CT)  704  are illustrated as residing at the connection interface  618 . The PT provides a voltage transformation such that voltage of conductor  606  may be sensed and communicated to voltage transducer  708 . The CT provides a current transformation such that current of conductor  606  may be sensed and communicated to current transducer  706 . Other voltage and/or current sensing devices may alternatively meter voltage and current, respectively. 
   Voltage transducer  708  converts the sensed alternating current (AC) voltage into a signal, such as a digitally sampled signal, that is proportional to the sensed AC voltage from the PT. Sensed voltages may be phase-to-phase voltages or phase-to-neutral voltages. Current transducer  706  converts the sensed AC current into a signal, such as a digitally sampled signal, that is proportional to the sensed AC current from the CT. 
   As noted previously, PTs and CTs are typically available at various points along an electric power system when various other functions associated with operation of the electric power system are performed. Thus, information corresponding to the voltage and information corresponding to the current may be available from already-available devices for some embodiments of the ANN source model identifier  600 . In other situations PTs, CTs, voltage transducers  708  and/or current transducers  706  may not be available. Accordingly, other embodiments of the ANN source model identifier  600  may be equipped with such devices, or such devices may be separately installed for the ANN source model identifier  600 . 
   As illustrated in  FIG. 7 , the ANN  700  receives a signal corresponding to metered current, via connection  710 . The ANN  700  is configured to output a predicted voltage {circumflex over (v)}, via the logical connection  712  (since the predicted voltage {circumflex over (v)} is computationally determined by executing software, the ANN logic  908  illustrated in  FIG. 9 , connection  712  is not a physical connection). The predicted voltage {circumflex over (v)} is input into the summing circuit  702  such that the predicted voltage {circumflex over (v)} is summed with the signal corresponding to metered voltage, provided on connection  714 , such that a difference between the predicted voltage {circumflex over (v)}, and the metered voltage is determined. The output of the summing circuit  702  (the difference between the predicted voltage {circumflex over (v)} and the metered voltage) is the voltage error signal, e, output on the logical connection  716 . The voltage error signal e is returned to the ANN  700 . 
   As the voltage error signal e approaches zero, or converges to within a predefined threshold, it is appreciated that the ANN  700  has completed training with respect to predicting voltage. That is, the ANN  700  is accurately predicting voltage metered at connection interface  618 , within some predefined threshold. 
   When predicted voltage {circumflex over (v)} is accurately predicted, the ANN  700  may then computationally determine the harmonic voltage component, {circumflex over (v)}—distorted. Information corresponding to the harmonic voltage component, {circumflex over (v)}—distorted, is output on connection  718 , and may be used for a variety of purposes, as is well understood in the arts. 
     FIG. 8  is a conceptual block diagram illustrating an embodiment of the ANN source model identifier  600 . The ANN  700  (see also  FIG. 7 ), in this embodiment, includes two artificial neural networks, the training ANN  802  and the estimating ANN  804 . It is appreciated that the training ANN  802 , the estimating ANN  804  and the summing circuit  802  may be implemented as software, firmware, or combinations of software and firmware. 
   In this conceptual embodiment, the training ANN  802  receives the signal corresponding to metered current, via connection  710 . At the end of a training time, or sampling interval, the training ANN  802  outputs the predicted voltage {circumflex over (v)}, via connection  712 , to the summing circuit  702 . Metered voltage is then summed with the predicted voltage {circumflex over (v)} (such that a difference between the predicted voltage {circumflex over (v)} and the metered voltage is determined). In one embodiment, the voltage at the end of the sampling interval is used for the comparison. (In other embodiments, the metered voltage used for training may be used when this information is stored for later retrieval.) 
   The voltage error signal e is returned on connection  716  to the training ANN  802 . As noted above, when the voltage error signal e approaches zero, or converges, the training ANN  802  has completed training with respect to predicting voltage. In some embodiments, the voltage error signal e has converged when the difference between the predicted voltage {circumflex over (v)} and the metered voltage is less than and/or equal to a predefined threshold. 
   The metered voltage may be considered as a pure sinusoidal wave provided by the source equivalent circuit  604  with harmonic components injected thereon by the non-linear load  616  ( FIGS. 6 and 7 ). Thus, the corresponding predicted voltage {circumflex over (v)} is a computed sinusoidal wave voltage distorted by the harmonics. The computed predicted voltage {circumflex over (v)} may be represented by any suitable mathematical equation or algorithm, which is selected based upon the design of the particular embodiment of the ANN source model identifier  600 . 
   In one embodiment, the source equivalent circuit  604  impedance is modeled. Accordingly, the impedance of the source equivalent circuit  604  is modeled by a suitable equation with determined weights. In another embodiment, weights associated with the equation defining the predicted voltage {circumflex over (v)} are determined. After the weights have been determined by the training ANN  802 , the weights are communicated to the estimating ANN  804  (represented conceptually by the directional arrow  806 ). 
   While the estimating ANN  804  determines the harmonic voltage component ({circumflex over (v)}—distorted), the training ANN  802  in one embodiment continues sampling. Weights are adjusted at each sample depending on the degree of convergence (the difference between the predicted voltage {circumflex over (v)} and the metered voltage). When the estimating ANN  804  has determined the harmonic voltage component ({circumflex over (v)}—distorted), the estimating ANN  804  receives another set of weights from the training ANN  802 . In one embodiment, the received second set of weights are the most current value of the weights determined by the training ANN  802 . Then, the estimating ANN  804  determines another harmonic voltage component based upon the new weights. The above-described process may be continuously repeated. 
   Depending upon the embodiment, the training ANN  804  may then restart the training process to predict another predicted voltage {circumflex over (v)}. Thus, after some period of time, weights associated with the subsequent predicted voltage {circumflex over (v)} are determined. The newly determined weights may or may not equal the previously determined weights, depending upon whether the nature of the non-linear load  616  and/or the power supply have changed by the time the second training period has been completed. That is, if the degree of convergence has changed since the previous sampling period, newly computed weights will be different. For example, a customer may have turned on (or off) a solid-state load, thereby changing the nature of the non-linear load  616 , thereby resulting in a change in the degree of convergence. Accordingly, the training and determining harmonic components may be performed on a continual basis. 
   The estimating ANN  804  receives the determined weights (for the source equivalent circuit  604  impedance and/or predicted voltage {circumflex over (v)} equation). The estimating ANN  804  then computationally applies the weights to a pure sinusoidal wave  808 . Distortion in the output of the sinusoidal wave (distorted by the determined weights) is then used to determine the harmonic voltage component ({circumflex over (v)}—distorted) associated with the source equivalent circuit  604 . 
   Information corresponding to the harmonic voltage component ({circumflex over (v)}—distorted) and/or a determined impedance associated with the source equivalent circuit  604  may then be communicated through connection interface  718 , and may be used for a variety of purposes, as is well understood in the arts. 
     FIG. 9  is a block diagram illustrating in greater detail an exemplary embodiment of an ANN source model identifier  600 . The exemplary embodiment comprises a processing system  902  and summing circuit  702 . Processing system  902  includes a processor  904  and memory  906 . In one embodiment, memory  906  includes regions for storing the summing circuit  702  (implemented as logic), artificial neural network (ANN) logic  908 , the above-described weights  910  and admittance  912 , data corresponding to the voltage error signal e  914 , the predicted voltage {circumflex over (v)}  916 , and/or the harmonic voltage component ({circumflex over (v)}—distorted)  918 , depending upon the particular embodiment. The information is saved into memory  906  for ongoing processing, and/or may be saved for later processing and/or analysis. In other embodiments, the information may be saved in other memories as needed, such as buffers or random access memories. 
   Also included are suitable interfaces that provide coupling to the above-described connections which communicate the various information. The metered current interface  920  provides coupling to connection  710  such that information corresponding to the metered current is received. The metered voltage interface  922  provides coupling to connection  714  such that information corresponding to the metered voltage is received. The output device interface  924  provides coupling to connection  718  such that information corresponding to the harmonic voltage component ({circumflex over (v)}—distorted) is transmitted to a suitable device. 
   The above described components are communicatively coupled to each other via bus  926  and connections  928 . In alternative embodiments of processing system  902 , the above-described components are connectively coupled to processor  904  in a different manner than illustrated in  FIG. 9 . For example, one or more of the above-described components may be directly coupled to processor  904  or may be coupled to processor  904  via intermediary components (not shown). 
   The above-described interfaces  920 ,  922  and/or  924  may be passive devices, or they may be active devices that process the received/transmitted signals into a suitable format. For example, the metered current interface  920  may be a simple coupling to connection  710  when the current transducer  706  ( FIG. 7 ) provides a suitable metered current signal that may be directly communicated onto bus  928  such that information corresponding to metered current is received by processor  904 . In another embodiment, the metered current interface  920  processes the metered current information received on connection  710  into a suitable signal that is directly communicated onto bus  928 . As another example, the output device interface  926  may communicate information corresponding to the harmonic voltage component ({circumflex over (v)}—distorted) to a display such that a voltage or current signal is displayed to a viewer. Or, output device interface  926  may communicate information corresponding to the harmonic voltage component ({circumflex over (v)}—distorted) to another processing system or memory for future analysis. 
   In various embodiments, the summing circuit  702  could be implemented as software (with its own processor and memory, not shown), as firmware, or as a combination of firmware and software. In another embodiment, an interface (not shown) to receive the metered voltage information on connection  714  is included such that the processor  904  computationally determines the voltage error signal e. In this embodiment, the interfaces  922  and  924  are omitted. 
     FIG. 10  shows a flow chart  1000  illustrating a process used by an embodiment of ANN source model identifier  600  ( FIG. 6-FIG .  9 ). The flow chart  1000  of  FIG. 10  shows the architecture, functionality and operation of an embodiment for implementing the ANN logic  908  ( FIG. 9 ). An alternative embodiment implements the logic of flow chart  1000  with hardware configured as a state machine. In this regard, each block may represent a module, segment or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in alternative embodiments, the functions noted in the blocks may occur out of the order noted in  FIG. 10 , or may include additional functions. For example, two blocks shown in succession in  FIG. 10  may in fact be substantially executed concurrently, the blocks may sometimes be executed in the reverse order, or some of the blocks may not be executed in all instances, depending upon the functionality involved, as will be further clarified hereinbelow. All such modifications and variations are intended to be included herein within the scope of this disclosure. 
   The process begins at block  1002 . At block  1004 , voltage on a source equivalent circuit is metered. At block  1006 , current on the source equivalent circuit is metered. At block  1008 , a predicted voltage based upon the metered current is determined. At block  1010 , the predicted voltage is compared with the metered voltage. At block  1012 , a harmonic voltage component is determined using at least one weight determined when the predicted voltage converges with the metered voltage. The process ends at block  1014 . 
   Embodiments of the ANN logic  408 , and the above-described information, implemented in memory  406  ( FIG. 4 ) and/or the ANN logic  908 , and the above-described information implemented in memory  906  ( FIG. 9 ) may be implemented using any suitable computer-readable medium. In the context of this specification, a “computer-readable medium” can be any means that can store or communicate the data associated with, used by or in connection with the instruction execution system, apparatus, and/or device. The computer-readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device now known or later developed. 
   Processing system  402  and/or processing system  902  are typically a commercially available processor. Examples of commercially available processors include, but are not limited to, a Pentium microprocessor from Intel Corporation, Power PC microprocessor, SPARC processor, PA-RISC processor or 68000 series microprocessor. Many other suitable processors are also available. Or, processing system  402  may be a specially designed and fabricated processor in accordance with embodiments of the ANN non-linear load analyzer  100 . Similarly, processing system  902  could be a specially designed and fabricated processor in accordance with embodiments of the ANN source model identifier  600 . 
   It should be emphasized that the above-described embodiments are merely examples of the disclosed system and method. Many variations and modifications may be made to the above-described embodiments. All such modifications and variations are intended to be included herein within the scope of this disclosure.