Patent Publication Number: US-10325150-B2

Title: System and method for electric load identification and classification employing support vector machine

Description:
This invention was made with Government support under DE-EE0003911 awarded by the Department of Energy National Energy Technology Laboratory. The Government has certain rights in this invention. 
    
    
     CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is related to commonly assigned, copending U.S. patent application Ser. No. 13/304,783, filed Nov. 28, 2011, entitled “System And Method Employing A Hierarchical Load Feature Database To Identify Electric Load Types Of Different Electric Loads”; 
     U.S. patent application Ser. No. 13/304,758, filed Nov. 28, 2011, entitled “System And Method Employing A Self-Organizing Map Load Feature Database To Identify Electric Load Types Of Different Electric Loads”; and 
     U.S. patent application Ser. No. 13/304,834, filed Nov. 28, 2011, entitled “System And Method Employing A Minimum Distance And A Load Feature Database To Identify Electric Load Types Of Different Electric Loads”. 
     BACKGROUND 
     Field 
     The disclosed concept pertains generally to electric loads and, more particularly, to methods of identifying electric load types of electric loads. The disclosed concept also pertains to systems for identifying electric load types of electric loads. 
     Background Information 
     Electric loads in residential and commercial building sectors account for over 75% of the total electricity consumption in the United States in 2009. Electric loads are commonly divided into several groups, such as for example and without limitation, space conditioning loads, water heating loads, ventilation loads, major appliances, lighting loads, and miscellaneous electric loads (MELs). MELs are the wide and diverse collection of portable and plug-in electricity-consuming devices along with all hard-wired electric loads that do not fit into other categories. TV sets and accessories, computers and accessories, portable equipment using chargers, and kitchen appliances are examples of typical MELs. Compared with any other single major category, MELs currently consume the largest portion of electricity. A recent report from the U.S. Department of Energy (DoE) predicts that MELs consumption will increase by an average of 2.3 percent per year and, in 2035, will account for 40 percent of the total electricity consumption in the commercial sector. 
     MELs&#39; relatively large portion in electricity consumption leads to increasing needs and opportunities of energy management and saving. A recent U.S. DoE research program, called Building America, aims at 50% energy savings in new homes by 2015 and 100% savings (zero net energy) by 2020. This program has started to identify and develop advanced solutions that can significantly reduce MELs&#39; power consumption. Moreover, granular load energy consumption and performance information is desired to accelerate the path toward smarter building energy intensity reduction, demand response, peak shaving, and energy optimization and saving. 
     MELs present special challenges because their operations are mainly under the need and control of building occupants. Without advanced control and management, MELs can constitute 30% to 40% of the electricity use in homes. Furthermore, MELs are distinct from other load categories as many MELs are of notable importance in daily life. For instance, a circuit protection device on an uninterruptable power supply (UPS) or at the input to a power strip interrupts all downstream power circuits when an overcurrent fault happens, but such an unexpected power interruption will cause sensitive equipment, such as a plugged-in desktop computer, to lose all current memory-based work. 
     MELs currently consume more electricity than any other single end-use service. MELs provide granular energy consumption and performance information to meet rising needs and opportunities of energy saving, demand response, peak shaving, and building management. A reliable intelligent method and system to identify different MELs is desired. 
     Several methods have been proposed to non-intrusively identify electric loads. Such known methods mainly consist of two major categories. Steady-state features, such as instantaneous real power, power factor, V-I trajectory and harmonic components, are extracted from voltage and current measurements. In the first category, some methods compare these features and their variations with a predefined database. In the second category, some methods adopt computational intelligent (CI) algorithms, such as radial basis functions (RBF), particle swarm optimization (PSO) and artificial neural networks (ANN). 
     The former category has disadvantages in accuracy, robustness and applicability. For instance, different MELs with similar front-end power supply units are not distinguishable in this manner. Also, few of the known methods are specifically designed for MELs. As a result, products on market, such as Navetas™ and enPowerMe™, are restricted to only a limited number of MELs. 
     The latter category suffers from the lack of knowledge during training and computational cost, which limits its applicability. 
     Moreover, the rapid development of power supply designs brings challenges to load identification. One type of MEL may be supplied by different power supply topologies. Therefore, a reliable load identification algorithm should be able to identify loads with diverse specifications, such as manufacturer and power rating. 
     A support vector machine (SVM) is a well known concept in computer science for a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis. A SVM has discriminative power for static classification problems and the capability to construct flexible decision boundaries. The standard SVM takes a set of input data made up of training examples and predicts, for each given input example, which of two possible classes the input belongs. This makes the SVM a non-probabilistic binary linear classifier. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns the examples into one category or the other. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to one of the categories based on which side of the gap they fall on. 
     A SVM constructs a hyperplane or set of hyperplanes in a high- or infinite-dimensional space, which can be used for classification, regression, or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. 
     Whereas an original problem may be stated in a finite dimensional space, called the original space, it often happens that the sets to be mapped are not linearly separable in that space. For this reason, the original finite-dimensional space is mapped into a much higher-dimensional space, making the separation easier in that space. To keep the computational load reasonable, the mapping used by SVM schemes are designed to ensure that dot products may be computed easily in terms of the variables in the original space, by defining them in terms of a kernel function K(x,y) selected to suit the problem. The hyperplanes in the higher dimensional space are defined as the set of points whose inner product with a vector in that space is constant. The vectors defining the hyperplanes can be chosen to be linear combinations with parameters α i  of images of feature vectors that occur in the database. With this choice of a hyperplane, the points x in the feature space that are mapped into the hyperplane are defined by the relation: 
     
       
         
           
             
               
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     If K(x,y) becomes small as y grows further from x, then each element in the sum measures the degree of closeness of the test point x to the corresponding database point x i . In this way, the sum of kernels above can be used to measure the relative nearness of each test point to the data points originating in one or the other of the sets to be discriminated. The set of points x mapped into any hyperplane can be quite convoluted as a result allowing much more complex discrimination between sets which are not convex at all in the original space. 
     In the case of support vector machines (SVMs), a data point is viewed as a p-dimensional vector (a list of p numbers), and the goal is to separate such points with a (p−1)-dimensional hyperplane. This is called a linear classifier. There are many hyperplanes that might classify the data. One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes. The hyperplane is chosen so that the distance from it to the nearest data point on each side is maximized. If such a hyperplane exists, then it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum margin classifier; or equivalently, the perception of optimal stability. 
     There is room for improvement in methods of identifying electric load types of a plurality of different electric loads. 
     There is also room for improvement in systems for identifying electric load types of a plurality of different electric loads. 
     SUMMARY 
     These needs and others are met by embodiments of the disclosed concept, which employ support vector machine (SVM) based identification for miscellaneous electric loads and apply either only SVM, or a combination of SVM and a supervised self-organizing map (SSOM). 
     In accordance with one aspect of the disclosed concept, a method of identifying electric load types of a plurality of different electric loads comprises: providing a support vector machine load feature database of a plurality of different electric load types; sensing a voltage signal and a current signal for each of the different electric loads; determining a load feature vector including at least six steady-state features with a processor from the sensed voltage signal and the sensed current signal; and identifying one of the different electric load types by relating the load feature vector including the at least six steady-state features to the support vector machine load feature database. 
     The method may comprise training the support vector machine load feature database as a multi-class one-against-all support vector machine for each of a plurality of different load classes or for each of the different electric load types. 
     The method may employ the at least six steady-state features selected from the group consisting of RMS current value, displacement power factor, total harmonic distortion of current, power factor, current crest factor, current K-factor, admittance, and normalized current third and fifth harmonics. 
     As another aspect of the disclosed concept, a method of identifying electric load types of a plurality of different electric loads comprises: providing a database including a first layer formed by a supervised self-organizing map database and a second layer formed by a support vector machine database; clustering a plurality of different load classes having a plurality of different load features in the first layer; providing a plurality of different electric load types under each of the different load classes in the second layer; placing different ones of the different electric load types having similar load feature vectors into a same one of the different load classes; sensing a voltage signal and a current signal for each of the different electric loads; determining a load feature vector including a plurality of steady-state features with a processor from the sensed voltage signal and the sensed current signal; and identifying by a support vector machine one of the different electric load types by relating the determined load feature vector including the steady-state features in the second layer of the database. 
     The method may further comprise: training the supervised self-organizing map database employing data corresponding to the different electric load types; training the support vector machine database as a multi-class one-against-all support vector machine for each of the plurality of different load classes; identifying the one of the different electric load types as being in one of the plurality of different load classes with the supervised self-organizing map database; and identifying the one of the different electric load types with the trained support vector machine database for the one of the plurality of different load classes. 
     The method may extract information from the trained support vector machine database and store simplified information in a trained neuron grid; employ as the different load classes a plurality of different load categories; determine one of the different load categories employing the determined load feature vector; and employ a support vector machine discriminator function for each the different load categories to identify the one of the different electric load types. 
     As another aspect of the disclosed concept, a system for identifying electric load types of a plurality of different electric loads comprises: a database including a first layer formed by a supervised self-organizing map database and a second layer formed by a support vector machine database, a plurality of different load classes having a plurality of different load features being clustered in the first layer, a plurality of different electric load types being under each of the different load classes in the second layer, different ones of the different electric load types having similar load feature vectors being placed into a same one of the different load classes; a plurality of sensors structured to sense a voltage signal and a current signal for each of the different electric loads; and a processor structured to determine a load feature vector including a plurality of steady-state features from the sensed voltage signal and the sensed current signal, and identify by a support vector machine one of the different electric load types by relating the determined load feature vector including the steady-state features in the second layer of the database. 
     The processor may be further structured to train the supervised self-organizing map database employing data corresponding to the different electric load types, train the support vector machine database as a multi-class one-against-all support vector machine for each of the plurality of different load classes, identify the one of the different electric load types as being in one of the plurality of different load classes with the supervised self-organizing map database, and identify the one of the different electric load types with the trained support vector machine database for the one of the plurality of different load classes. 
     The processor may be further structured to extract information from the trained support vector machine database and store simplified information in a trained neuron grid, employ as the different load classes a plurality of different load categories; determine one of the different load categories employing the determined load feature vector, and employ a support vector machine discriminator function for each the different load categories to identify the one of the different electric load types. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A full understanding of the disclosed concept can be gained from the following description of the preferred embodiments when read in conjunction with the accompanying drawings in which: 
         FIG. 1  is a diagram of a support vector machine (SVM) framework employing kernel functions. 
         FIGS. 2A and 2B  are plots of voltage and current profiles, respectively, of a DVD player. 
         FIGS. 2C and 2D  are plots of voltage and current profiles, respectively, of an LCD TV. 
         FIGS. 2E and 2F  are plots of voltage and current profiles, respectively, of an oscillating fan. 
         FIGS. 3A and 3B  are plots of an output grid of a supervised Self-Organizing Map (SSOM) using a set of six example steady-state features and harmonics as features, respectively. 
         FIG. 4  is a block diagram of a cross validation mechanism for testing performance of a one-against-all SVM identifier in accordance with an embodiment of the disclosed concept. 
         FIG. 5  is a block diagram of a support vector machine (SVM) based load classification/identification system in accordance with embodiments of the disclosed concept. 
         FIG. 6  is a flowchart of a hybrid SSOM/SVM classifier process in accordance with an embodiment of the disclosed concept. 
         FIG. 7  is a block diagram of a self-organizing map (SSOM)/support vector machine (SVM) based load classification/identification system in accordance with embodiments of the disclosed concept. 
         FIGS. 8A and 8B  are plots of voltage and current profiles, respectively, of an LCD TV used by a trained SSOM in accordance with an embodiment of the disclosed concept. 
         FIGS. 8C and 8D  are plots of voltage and current profiles, respectively, of an LED TV used by a trained SSOM in accordance with an embodiment of the disclosed concept. 
         FIGS. 8E and 8F  are plots of voltage and current profiles, respectively, of a plasma TV used by a trained SSOM in accordance with an embodiment of the disclosed concept. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     As employed herein, the term “number” shall mean one or an integer greater than one (i.e., a plurality). 
     As employed herein, the term “processor” shall mean a programmable analog and/or digital device that can store, retrieve, and process data; a computer; a workstation; a personal computer; a microprocessor; a microcontroller; a microcomputer; a central processing unit; a mainframe computer; a mini-computer; a server; a networked processor; or any suitable processing device or apparatus. 
     The disclosed concept provides a support vector machine (SVM) based advanced identification system and method for miscellaneous electric loads (MELs). Embodiments including applying only SVM as well as a combination of SVM and a supervised self-organizing map (SSOM) are disclosed. This applies the supervised SSOM to classify and identify MELs. A relatively large number of MELs are classified into several clusters. Within each cluster, a SVM can be applied to separate MELs with similar but not identical features. 
     In the second embodiment, SSOM first clusters a relatively large number of MELs into several classes. MELs with similar feature values fall into the same class. SVM is then employed to identify similar MELs. The combination of SVM and SSOM shows satisfactory accuracy in tests using real-world data. The hybrid SSOM/SVM identifier can achieve better performance in the sense of accuracy, robustness and applicability. The SSOM identifier first extracts information from a relatively large amount of training data and stores that simplified information in a trained neuron grid. When an input feature vector is presented to the hybrid SSOM/SVM identifier, it first determines which load category it falls into and then utilizes an SVM discriminator function for each category to make an identification decision. 
     Multi-Class Support Vector Machine (SVM) Classifier 
     A method for multi-class classification is known from basic SVMs. In the disclosed concept, a MEL identification method employs a SVM based load feature database formed from sensed current and voltage signals. Basic SVMs are inherently designed for classification of two classes, ω 1  and ω 2 , of patterns which are described by feature vectors extracted from data in a predefined manner. If x is such a feature vector, then the SVM utilizes a (typically nonlinear) mapping from an input feature vector space to a high-dimensional (possibly infinite-dimensional) Euclidean space H, as shown by Equation 1.
 
Φ: x∈R   l →Φ( x )∈ H    (Eq. 1)
 
wherein:
 
     x is a feature vector of dimension l; 
     R is the standard notation of the vector space of real numbers; 
     R l  is the vector space of real numbers (of dimension l); and 
     Φ is a mapping function, mapping from the l-dimension space of real numbers to the space H. 
     In turn, the two classes can be satisfactorily separated by a hyperplane as defined by Equation 2.
 
 g ( x )=ω T   x+ω   0    (Eq. 2)
 
wherein:
 
     ω and ω o  are co-efficients defining the hyperplane g(x); 
     superscript T denotes the transpose of a vector; and 
     g(x) is a function describing a hyperplane in high dimensional space. 
     Both x and ω, for example, are 2-by-1 vectors, then ω T  is of dimension 1-by-2, and g(x) is a straight line in two-dimensional space. 
     Once an optimal hyperplane (ω, ω 0 ) has been determined, classification of which class an unknown feature vector x* (i.e., a different vector other than x) belongs to is performed based on the sign of g(x*). The SVM training algorithm only depends on the training data through inner products in H (i.e., on functions of the form shown in Equation 3).
 
 K ( x   i   ,x   j )= Φ( x   i ),Φ( x   j )    (Eq. 3)
 
wherein:
 
     K is usually called a kernel function; it is common that an SVM needs only to specify K before its training instead of knowing the explicit form of Φ; 
     x i , i=1, 2, . . . , are feature vectors in the training data; for each x i  denote the corresponding class indicator by y i  (+1 for ω 1  and −1 for ω 2 ); and 
     the function K needs two inputs, thus there is another index besides i, that index is j. 
     Once an appropriate kernel has been adopted, the optimal hyperplane (ω, ω 0 ) can be determined from Equation 4: 
                     max   λ     ⁢     (         ∑   i             ⁢           ⁢     λ   i       -       1   2     ⁢       ∑     i   ,   j               ⁢           ⁢       λ   i     ⁢     λ   j     ⁢     y   i     ⁢     y   j     ⁢     K   ⁡     (       x   i     ,     x   j       )               )             (     Eq   .           ⁢   4     )               
subject to Equation 5:
 
                       0   ≤     λ   i     ≤   C     ,     
     ⁢         ∑   i             ⁢       λ   i     ⁢     y   i         =   0       ⁢     
     ⁢       i   =   1     ,   2   ,     …   ⁢           ⁢   N               (     Eq   .           ⁢   5     )               
wherein:
 
     λ is the vector of nonnegative Lagrange multipliers λ i ; 
     C is a parameter to be chosen by the user with a relatively larger C corresponding to assigning a relatively higher penalty to errors; and 
     N is the total number of Lagrange multipliers, N=l, i.e., the dimension of feature vector x. 
     The resulting classifier assigns x to ω 1  (ω 2 ) if Equation 6 is met. 
     
       
         
           
             
               
                 
                   
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       FIG. 1  depicts a SVM framework  2  that employs kernel functions, K(.,.) 4, 6, 8, where each x i    10  is an input to one of the kernel functions, each λ i y i    12  is a weight to the output of one of the functions, and ω 0    14  is the bias. 
     For multi-class SVM kernel selection, which is an M-class problem, common extensions are to either consider it as a set of M two-class problems (one-against-all) or train M(M−1)/2 basic SVM classifiers (one-against-one). In the disclosed concept, the one-against-all technique is employed. For each ω i  of the M classes, the one-against-all SVM aims at determining an optimal discriminator function, g i (x), i=1, 2, . . . , M, so that g i (x)&gt;g j (x) for all j≠i and x∈ω i . 
     The classification rule is then defined by Equation 7. 
                     x   ∈     ω   i       ⁢     
     ⁢       if   ⁢           ⁢   i     =     arg   ⁢           ⁢       max   k     ⁢     {       g   k     ⁡     (   x   )       }                   (     Eq   .           ⁢   7     )               
wherein:
 
     k is an index other than i, since here i is the output number, and k denotes the index during the searching of such an output i. 
     Many kernels are available for SVM, such as polynomials, RBF, and hyperbolic tangent. In the disclosed concept, the Gaussian RBF kernel is employed, which is also the most commonly adopted kernel in pattern recognition problems. 
     Electric Load Identification Framework Using Only SVM 
     Known proposals apply SVM to identify harmonic sources. The features used in these proposals are high frequency harmonic components. 
     For the purpose of MELs identification, a different set of steady-state features with practical meaning are adopted with the voltage (V(t)) and current (I(t)) waveforms represented by Fourier series of the form shown by Equations 8 and 9, respectively. 
                     V   ⁡     (   t   )       =       ∑     k   =   1     ∞     ⁢           ⁢       V   k     ⁢     sin   ⁡     (       k   ⁢           ⁢     ω   0     ⁢   t     +     δ   k       )                   (     Eq   .           ⁢   8     )                 I   ⁡     (   t   )       =       ∑     k   =   1     ∞     ⁢           ⁢       I   k     ⁢     sin   ⁡     (       k   ⁢           ⁢     ω   0     ⁢   t     +     θ   k       )                   (     Eq   .           ⁢   9     )               
wherein:
 
     ω 0  is frequency; 
     k is an index of the order of harmonics; and 
     δ k , θ k  are the phase angles of the k-th order harmonic. 
     The following six steady-state features are considered. 
     First, the RMS current value, I RMS , gives equivalent information about the average power. 
     Second, the average displacement power factor is shown by Equation 10.
 
 pf   disp =cos(δ 1 −θ 1 )   (Eq. 10)
 
wherein:
 
     pf disp  is the displacement power factor; and 
     (δ 1 −θ 1 ) is the fundamental power factor angle. 
     Third, the average total harmonic distortion (THD) of current (THD i ) is shown by Equation 11. 
                     THD   I     =             ∑     k   =   2     ∞     ⁢     I   k   2           I   1       ×   100   ⁢   %             (     Eq   .           ⁢   11     )               
wherein:
 
     k is an index of the order of harmonics; 
     I k  is the k-th order harmonic in current; and 
     I 1  is the fundamental, i.e., first order harmonic. 
     Fourth, the average power factor (pf) is determined by calculating displacement power factor and the current THD using the fast Fourier transform (FFT) of the current waveform as shown by Equation 12. 
     
       
         
           
             
               
                 
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     Fifth, the crest factor (cf) or peak-to-average ratio (PAR) is determined by Equation 13. 
                   cf   =            I   peak            I   RMS               (     Eq   .           ⁢   13     )               
wherein:
 
     I peak  is the current&#39;s peak amplitude; and 
     I rms  is the current&#39;s RMS value. 
     The example current crest factor or PAR or peak-to-RMS power ratio (PAPR) is a measurement of a waveform, calculated from the peak amplitude of the waveform divided by the RMS value of the waveform. It is therefore a dimensionless quantity. Crest factor can be used to detect the existence of a current pulse. A sharp peak corresponds to a relatively higher value of crest factor. 
     Sixth, there are the normalized 3 rd  (and 5 th ) harmonics of current. 
     The fact that the above example set of steady-state features is better for the purpose of MELs identification can be validated by both SVM and SSOM. For three example MELs, a DVD player (D), an LCD TV (T) and a oscillating fan (F), their voltage profiles  22 , 26 , 30  and current profiles  24 , 28 , 32  are shown in  FIGS. 2A-2F . 
       FIGS. 3A-3B  show output grids  42 , 44  of SSOM using two sets of different features: (1) the disclosed set of six example steady-state features, and (2) harmonics as features. The SSOM output grid  42  gives a better clustered grid using the disclosed set of steady-state features. For SVM, unlike SSOM, the training output is parameters describing a high-dimensional hyper-plane, which cannot be visualized in two dimensions. 
     Example 1 
     Therefore, the advantage of the disclosed set of steady-state features over using harmonics is shown by tests as summarized in Table 1. In this example, the total number of available feature vectors for training and testing is 3600, and three different cases are tested and compared. The results are generated by solving multi-class one-against-all SVMs. This compares testing success rate of different features sets using a multi-class one-against-all SVMs. 
                             TABLE 1                          Success rate                                 270 points   540 points   1080 points           for training,   for training,   for training,           and 3330   and 3060   and 2520           points   points   points           for testing   for testing   for testing                                                 Disclosed     100%     100%     100%           set of six           example           steady-           state           features           Harmonics   99.56%   99.53%   99.43%           as features                        
It is clear to see from Table 1 that simply using harmonics cannot guarantee a 100% success rate even with only three relatively distinct MELs.
 
     Data was collected, processed and employed from commercially available MELs  48 . Steady-state features of an example set of 42 types of MELs, with 5 to 7 brands per type, were evaluated. For the purpose of accuracy and convenience for FFT, the sampling frequency of the data acquisition (DAQ) system was set to 30.72 kS/sec. Lower sampling frequencies, such as 7.68 kS/sec and 3.84 kS/sec, were also tested with the cross validation results being relatively the same. Similar to a known SSOM classifier, a cross validation mechanism tested the performance of the one-against-all SVM identifier  50 , as shown in  FIG. 4 . This shows the process of how to cross-validate the performance of a one-against-all SVM classifier  58 . The collected data  52  is divided into two sets: training  54  and testing  56 . The SVM classifier  58  is first trained by the training data and then performs identification  60  on the testing data. The results  62 , 64  are compared with the actual labeling of the testing data to evaluate the success rate  66  and the performance of the SVM identifier  50 . 
     Referring to  FIG. 5 , a system  70  provides V/I waveform measurement  72 , high-dimensional feature extraction  74 , a multi-class one-against-all SVM  76  employing load feature clustering by load types  78 , an SVM classifier construction  80 , and load identification results  82 . The system  70  also includes a processor  84  structured to determine at least six different load features from a sensed voltage signal and a sensed current signal for a corresponding one of a plurality of different electric loads, and identify, at  82 , a load type of the different electric load types by relating the different load features to the SVM database  76 . 
     Hybrid SSOM/SVM Classifier 
     The self-organizing map (SOM) is an unsupervised artificial neural network that is trained using competitive learning. That is, all neurons compete for the right to respond to the input data but only one neuron will win at a time. The training result of a basic SOM is a low-dimensional (typically two-dimensional), discretized grid of neurons with similar characteristics as the training samples. MELs that are similar or share common features in the input space are mapped to neurons that are positioned close to one another to form a cluster  90 , as shown in  FIG. 3A . With a relatively large number of MELs types, the output grid of the SSOM would get very crowded and thus unreadable, as shown in the output grid  44  of  FIG. 3B . 
     For the purpose of MELs identification, different types of loads with similar power supply units or features are partitioned into the same cluster. For example, DVD players and set-top boxes are very similar in both front-end power supply units and steady-state operating characteristics. Therefore, in a trained SSOM, DVD players and set-top boxes are classified into one cluster. However, the disadvantage is that it is difficult for a SSOM classifier to distinguish DVD players from set-top boxes. The SVM classifier  58  ( FIG. 4 ), on the other hand, performs well when handling similar, but not identical, sets of feature vectors. Therefore, a hybrid SSOM/SVM achieves better performance than only the SSOM classifier or only a multi-class one-against-all SVM classifier. 
     Based on front-end electronic circuit topologies of MELs, electrical operation principles or the functional nature of MELs, and user usage behaviors, MELs are divided into seven example load categories with distinct steady-state features: resistive loads (R); reactive predominant loads (X); electronic loads with power factor correction circuit (P); electronic loads without power factor correction circuit (NP); linear power supply using transformer to boost voltage (T); phase angle controllable loads (PAC); and complex structures (M). For example and without limitation, different electric load types having distinct steady-state features, such as resistive appliances, motor driven appliances, electronically fed appliances, non-linear loads with direct AC connections, and other unknown load types can be employed. 
     The architecture of the hybrid SSOM/SVM classifier  100  is shown in  FIG. 6 . First, MELs data are collected to form a database at  102 . Then, the SSOM is trained with all available MELs data to form a clustered grid, and a multi-class one-against-all SVM is trained for each of the seven example load categories at  104 . Next, an unknown MEL is identified as being one of the seven example load categories at  106 . Finally, using the trained SVM for the identified load category, the MEL type  109  is identified at  108 . 
     Referring to  FIG. 7 , a system  110  includes a processor  111 , V/I waveform measurement  112 , high-dimensional feature extraction  114 , a hybrid hierarchical SSOM/SVM  116  including a top-layer SSOM  118  providing load feature clustering by load categories  119  and a second-layer SVM  120  providing load feature clustering by load type under each load category  121 , SSOM/SVM classifier construction  122 , and load identification results  124 . For example and without limitation, the steady-state features of the determined load feature vector, as part of the high-dimensional feature extraction  114 , can include voltage-current trajectory features selected from the group consisting of area, eccentricity, and Hausdorff distance. 
     Area, A, refers to the area enclosed by a V-I trajectory. Area is proportional to the magnitude of the phase shift between the voltage and the current. If current leads voltage, then Area has a positive sign. If current lags voltage, then Area becomes negative. Area is directly calculated from the coordinates of the voltage and current points, (x i , y i ), on the V-I trajectory. 
     Eccentricity, E, is the measure of the aspect ratio of a shape, and is the ratio of the length of the major axis to the length of the minor axis. This feature helps to identify the shape of a voltage or current waveform. 
     The Hausdorff distance, or Hausdorff metric, also called Pompeiu-Hausdorff distance, measures how far two subsets of a metric space are from each other. It turns the set of non-empty compact subsets of a metric space into a metric space in its own right. The Hausdorff distance is the longest distance one can be forced to travel by an adversary who chooses a point in one of the two sets, from where you then must travel to the other set. In other words, it is the farthest point of a set that you can be to the closest point of a different set. 
     Example 2 
     Table 2 summarizes several tests to show the performance of the disclosed method and system  110  for MELs identification, including the testing success rate. These test the performance of the multi-class one-against-all SVM classifier  58  of  FIG. 4  on a relatively large number of MELs including a selection of typical MELs. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                   
                 Identification 
               
               
                   
                 MELs 
                 Success Rate (%) 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 Compact 
                 98.67 
               
               
                   
                 Fluorescent 
               
               
                   
                 Lights 
               
               
                   
                 Fluorescent 
                 100.00 
               
               
                   
                 Lights 
               
               
                   
                 Incandescent 
                 100.00 
               
               
                   
                 Lights 
               
               
                   
                 Fan 
                 100.00 
               
               
                   
                 Printer 
                 99.66 
               
               
                   
                 Cellphone 
                 100.00 
               
               
                   
                 Charger 
               
               
                   
                 DVD player 
                 98.66 
               
               
                   
                 Heater 
                 100.00 
               
               
                   
                 LCD TV 
                 99.72 
               
               
                   
                 LED TV 
                 93.33 
               
               
                   
                 Microwave 
                 100.00 
               
               
                   
                 Plasma TV 
                 89.66 
               
               
                   
                 Set Top Box 
                 100.00 
               
               
                   
                   
               
            
           
         
       
     
     Example 3 
     The multi-class one-against-all SVM classifier  58  of  FIG. 4  is compared against SSOM classifier performance with different amounts of data. One advantage of the disclosed multi-class one-against-all SVM classifier  58  is that it employs a relatively small amount of training data compared with other classifiers. Some test results containing three loads and different choices of cross-validation are shown in Table 3, in which the ratios between training data and testing data are indicated in the first column. The following tests are carried out when both classifiers are trained by feature vectors from 32 different models of 12 types of MELs, with 3600 feature points for each model. For all of the tests disclosed herein, 512-point FFTs are done to calculate the features. 
     
       
         
           
               
               
               
               
               
               
             
               
                   
                 TABLE 3 
               
               
                   
                   
               
               
                   
                 Success 
                   
                   
                   
                   
               
               
                   
                 Rate 
                 DVD 
                 TV 
                 Fan 
                 Total 
               
               
                   
                   
               
             
            
               
                   
                 SVM 
                   100% 
                 93.77% 
                   100% 
                 97.92% 
               
               
                   
                 (5% for 
               
               
                   
                 training, 
               
               
                   
                 95% for 
               
               
                   
                 test) 
               
               
                   
                 SVM 
                   100% 
                 93.43% 
                   100% 
                 97.81% 
               
               
                   
                 (10% for 
               
               
                   
                 training, 
               
               
                   
                 90% for 
               
               
                   
                 test) 
               
               
                   
                 SVM 
                   100% 
                   100% 
                   100% 
                   100% 
               
               
                   
                 (20% for 
               
               
                   
                 training, 
               
               
                   
                 80% for 
               
               
                   
                 test) 
               
               
                   
                 SVM 
                   100% 
                   100% 
                   100% 
                   100% 
               
               
                   
                 (30% for 
               
               
                   
                 training, 
               
               
                   
                 70% for 
               
               
                   
                 test) 
               
               
                   
                 SSOM 
                 49.36% 
                 81.99% 
                 97.34% 
                 76.23% 
               
               
                   
                 (5% for 
               
               
                   
                 training, 
               
               
                   
                 95% for 
               
               
                   
                 test) 
               
               
                   
                 SSOM 
                 91.30% 
                 87.81% 
                 97.69% 
                 92.26% 
               
               
                   
                 (10% for 
               
               
                   
                 training, 
               
               
                   
                 90% for 
               
               
                   
                 test) 
               
               
                   
                 SSOM 
                 96.74% 
                 94.03% 
                 93.92% 
                 94.90% 
               
               
                   
                 (20% for 
               
               
                   
                 training, 
               
               
                   
                 80% for 
               
               
                   
                 test) 
               
               
                   
                 SSOM 
                 99.75% 
                 99.83% 
                 98.00% 
                 99.19% 
               
               
                   
                 (67% for 
               
               
                   
                 training, 
               
               
                   
                 33% for 
               
               
                   
                 test) 
               
               
                   
                   
               
            
           
         
       
     
     From Table 3, it is clear to see that the multi-class one-against-all SVM classifier  58  can get a 100% testing success rate with only 20% of the total data, which is much better than a SSOM classifier. 
     Example 4 
     The following tests are for the hybrid SSOM/SVM classifier  100  of  FIG. 6 . These tests are carried out with the SSOM  118  ( FIG. 7 ) being trained by feature vectors from 32 different models of 12 types of MELs, with 3600 feature points for each model. In this case, the output grid of the SSOM  118  gets relatively very crowded and unreadable. Therefore, it is not shown. In the trained SSOM  118 , MELs with similar features are mapped into the same cluster. The voltage profiles  132 , 136 , 140  and current profiles  134 , 138 , 142  of three types of TVs (e.g., LCD, LED and plasma) that would be clustered together are shown in  FIGS. 8A-8F . These three MELs share similar front-end power supply units as well as feature vectors, and thus it is very difficult to distinguish between them. 
     The different types of TVs are grouped into one cluster by the SSOM classifier. However, the SSOM classifier gets an average success rate around only about 85% to identify each type of TV. In contrast, within the hybrid SSOM/SVM classifier  100 , the average testing success rate is greater than 95%. These success rates, for similar MELs, are shown in Table 4. 
     
       
         
           
               
               
               
               
               
             
               
                 TABLE 4 
               
               
                   
               
               
                 Success Rate 
                 LCD TV 
                 LED TV 
                 Plasma TV 
                 Average 
               
               
                   
               
             
            
               
                 SSOM 
                 80.17% 
                 97.85% 
                 85.25% 
                 85.28% 
               
               
                 identifier 
               
               
                 Hybrid 
                 98.30% 
                 78.89% 
                 98.96% 
                 92.05% 
               
               
                 SSOM/SVM 
               
               
                 identifier (20% 
               
               
                 data for 
               
               
                 training) 
               
               
                 Hybrid 
                 95.99% 
                 90.95% 
                 98.85% 
                 95.26% 
               
               
                 SSOM/SVM 
               
               
                 identifier (30% 
               
               
                 data for 
               
               
                 training) 
               
               
                   
               
            
           
         
       
     
     From Table 4, it is clear to see that the more training data for the SVM  120  in the hybrid SSOM/SVM identifier  116 , the better performance it has. But the SVM training in the hybrid SSOM/SVM identifier  116  requires far less data than a pure SSOM classifier. 
     Compared with methods, such as only SSOM or only SVM classifiers, the disclosed hybrid SSOM/SVM identifier  116  provides better performance in the sense of accuracy, robustness and applicability. The SSOM identifier  118  first extracts information from the relatively large amount of training data and stores that simplified information in the trained neuron grid. When an input feature vector is presented to the hybrid SSOM/SVM identifier  116 , it first determines which load category it falls into, and then employs an SVM discriminator function  120  for each category to get a robust and correct identification decision. 
     The disclosed concept provides a hybrid SSOM/SVM classifier  116  for the purpose of intelligent and nonintrusive MELs classification and identification with relatively high identification accuracy, robustness and applicability with respect to the diversity of different models of each type of MEL. This hybrid classifier  116  employs the power of an SSOM classifier  118  for MELs to first classify the relatively large amount of MELs models into several clusters. Within each cluster, a more accurate identification decision is made by a multi-class one-against-all SVM classifier  120 . This hybrid SSOM/SVM classifier  116  employs steady-state conditions. For MELs with similar power supply units, the disclosed hybrid SSOM/SVM identifier  116  provides hard (or absolute) decisions. Preferably, soft (or probabilistic) decisions should be provided for the electric load identification problem. 
     While specific embodiments of the disclosed concept have been described in detail, it will be appreciated by those skilled in the art that various modifications and alternatives to those details could be developed in light of the overall teachings of the disclosure. Accordingly, the particular arrangements disclosed are meant to be illustrative only and not limiting as to the scope of the disclosed concept which is to be given the full breadth of the claims appended and any and all equivalents thereof.