Classification of power quality disturbances using time-frequency ambiguity plane and neural networks

Classification of power quality disturbances using time-frequency ambiguity plane and neural networks Min Wang Piotr Ochenkowski Alexander Mamishev EEE Student Member EEE Member SEAL (Sensors, Energy, and Automation Laboratory) Department of Electrical Engineering University of Washington Box 352500, Seattle, WA 98195 Abstract dentification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection, This paper presents a new approach for classifying the events that represent or lead to the degradation of power quality. The concept of ambiguity plane together with modified Fisher s Discriminant Ratio Kernel is used for feature extraction. A neural network with feedforward structure is chosen as the classifier. The results of extensive simulations confirm the feasibility of the proposed algorithm. This novel combination of methods shows promise for further development of a fully automated power quality monitoring system. The potential of developing a more powerful fuzzy classification method based on this algorithm is also discussed. Keywords -- Power Quality Disturbances, Classification, Ambiguity Plane, Modified Fisher s Discriminant Ratio Kernel, Neural Network. NTRODUCTON The proliferation of highly sensitive computerized equipment places increasingly more stringent demands on the quality of electric power supplied to the customer. Not only must the power be supplied without interruption, but also the voltage and current waveforms must maintain nearly sinusoidal shape, constant frequency, and amplitude at all times to ensure continuous equipment operation. Low power quality can cause serious problems for the affected loads, such as short lifetime, malfunctions, instabilities, interruption, and etc. The key reason for our increasingly keen interests in power quality lies in the great economic value directly associated with those disturbances. Poor power quality is normally characterized by the presence of disturbances such as harmonics distortion, capacitor switching, high impedance faults, transformer inrush currents, lightning pulses, and motor starting transients. n order to improve the quality of service, electrical utilities must provide real-time monitoring systems that are able to identify the signatures of different events and make proper decisions for switching and maintenance. Existing methods for detection and classification of power system disturbances are laborious since they are primary based on visual inspection of waveforms [1]. Recent advances in signal processing and pattern recognition have led to the development of several new classification approaches, which are based on discrete wavelet transform, multiresolution signal decomposition, polynomial approximation, and bispectra analysis [1-4]. The new method presented in this paper is based on employing timefrequency ambiguity plane, modified Fisher s Discriminant Ratio Kernel, and artificial neural network. t shows very good classification performance on 6 categories of simulated disturbance signals. This novel combination of methods also brings up the possibility of discriminating transient type disturbances automatically and accurately.. POWERQUALTYDSTURBANCECLASSES There are various types of events that can degrade power quality, which makes the identification problems often elusive and difficult. n this paper we develop our classification algorithm based on disturbance events from six major categories, which are harmonics, capacitor high frequency switching, capacitor low frequency switching, voltage sudden sag, voltage gradual sag, and voltage swell, Figure 1 shows the example waveforms of these events, Figure 1(a) represents the waveform affected by harmonics. To some, harmonics distortion is still the most significant power quality problem [5]. Because of the increased popularity of electronic and other non-linear loads, such as adjustable-speed drives, arc furnaces, and induction

furnaces, perfect sinusoid waveforms quite often become distorted in this way. Current harmonics causes increased losses in the customer s and utility s power system components, while voltage harmonics affects not only sensitive electronic loads but also electric motors and capacitor banks. r Lrum s-2., 2 0 40 60 80 2 q2 ~ 0~w ) -20 20 60 80 l%(rns) 2, r - - r r o -c. o 20 40 60 80 g ~ (e) o 5.21 _o 20 40 60 80-2.. ~ o 20-40-- - 60 80 Time(ms) Figure 1. Sample disturbance signals: a) harmonics, b) fast switching transient, c) slow switching transient, d) sudden voltage sag, e) graduate voltage sag, f) voltage swell. Transients caused by capacitor switching are one of the most common sources of degradation in utility systems [5]. Two sample signals that correspond to capacitor high and low frequency switching are shown on Figure l(b) and 1(c), respectively. The frequency of a transient is determined by capacitance and inductance of the system. Although capacitors have the drawback of producing the oscillatory transients when switched, they are still used in power systems to correct the power factor. A momentary voltage dip that lasts for a few seconds or less is classified as voltage sag. t is caused by faults on the power systemor the start of large loads, such as motors, Figure 1(d) shows an example of voltage sudden sag. Also a voltage gradual decay sag signal is given on Figure 1(e). Process industry equipment can be particularly susceptible to problems with voltage sags because the equipment is interconnected and a trip of any component in the process (a) (d) can cause the whole plant to shut down. These industries include plastics, petrochemicals, textiles, paper, semiconductor, and rubber [6]. Figure 1(f) shows the signal affected by voltage swell. Swells are often caused by faults, capacitor ener.gization, and load switching.. BACKGROUND n pattern recognition applications, features are often extracted some form of time-frequency representations (TFRs). Among the class of correlative TFRs, ambiguity plane plays an important role. t has been used extensively in the fields of radar, sonar, radio astronomy, communications and optics [7], To achieve good classification results, the feature extraction scheme should be based on some TFR that is optimal for the classification task. The spectrogram is traditionally used with an underground assumption that a spectrogram is appropriate for the classification task. This assumption might not be appropriate and potentially degrade the classification performance, because the objective of a spectrogram is to describe the energy density of a signal simultaneously in time and frequency accurately, while the goal for classification is generating a TFR that maximizes the separability of TFRs from different classes. So it is desirable to design TFRs that specifically emphasize the differences between classes [8]. Gillespie and Atlas showed that a certain class of quadratic TFRs always provides best classification performance [9]. This class of ambiguity plane based TFRs discard any redundant information and retain only that information which is essential for classification. Gillespie and Atlas have successfully applied this technique on tool-wear monitoring and radar transmitter identification [9, 10]. Here in our algorithm for classifying power quality disturbances, the concept of ambiguity plane together with modified Fisher s Discriminant Ratio Kernel is used for feature extraction, essentially looking for certain form of TFR that fits the classification goal best. A neural network with feed forward structure is chosen as the classifier. V. CLASSFCATONALGORTHM The diagram in Figure 2 shows the complete classification algorithm we proposed. (The transient signal picture in this figure is taken from the website of Electrotek nc.) n our study, each voltage signal to be identified consists of five cycles of voltage waveform sampled 256 times per cycle, with up to 0.5 YO added randomly generated noise. The target of the feature extraction is to generate a N-point feature vector from the original 1280-point voltage signal. The feature vectors are the inputs of the neural network for classification.

m!r[yt,r] = x *[n]x[((l z +T))N ] (1) f we express an example voltage signal as, V=[Vl V2 V3... V2Vn_lvfi]i] (2) The instantaneous autocorrelation function (AF) will be, nstantaneous Autocorrelation Function 2 2 2 2 v v h V2 n l v, r+ V2. V3. n-1 n n 1 n Z=() r=l Time-Frequency Ambiguity Plane AF = V,. V3 V2V4 v, v.-, ~l.vn..... V2. V,. [ v, V. n 1 1 n 2 1 v.-, v.-, v V*.2 vfl vn., v. ) Vu-2 ~=~ z r=~-l =2 (3) Modified Fisher s Discriminant Ratio 1 Kernel n our case, n is equal to 160. C. Ambiguity Plane * F==l (a) Figure 2. Classification Algorithm (c) A. Lowpass Filtering and Resampling As depicted in Figure 2, the first step is to pass the original signal through a Lowpass filter and resample the signal with a downsampling rate 8. By downsampling, the signal dimension has been reduced greatly, which leads to a dramatic reduction of the computation complexity. n addition, electrical noise has also been attenuated. Using Lowpass filter is to avoid aliasing during downsampling. After this step, a new 160-point signal that keeps the signature of the original signal is obtained. B. nstantaneous Autocorrelation Function Now we calculate the instantaneous autocorrelation function R/n,t]for the signal x, (e) Figure 3. Ambiguity Planes for Different PQ Events a) Harmonics; b) Capacitor fast switching; c) Capacitor slow switching; d) Sudden sag; e) Gradual sag decay; f,)swell. Here the horizontal and vertical axes are discrete Doppler q and lag ~. The time-frequency ambiguity plane of the original signal can be obtained by taking inverse Fourier transform on the AF as follows,

Here q and ~ are discrete Doppler and lag respectively. Ambiguity plane is a very important concept of our feature extraction process. We will construct the TFR optimal for our classification task by smoothing the ambiguity plane with a class-dependant kernel. Here the dimension of ambiguity plane is 160*160. Basically we will directly select N points from this plane as our feature vector. Before we describe how to select features vectors, let s look at the ambiguity planes corresponding to different classes of signals in Figure 3 and get some intuitions about it. We find the ambiguity planes corresponding to different classes show different patterns. The ambiguity plane has very desirable properties for classification. An individual location in this plane captures global information about the time frequency structure of the signal. Points on the axis q=o result in time-frequency structure that is stationary in time. Similarly, points on the ~=0 axis correspond to a timefrequency structure that is stationary in frequency [7]. D. Modified Fisher s Discriminant Ratio Kernel There is a distinct ambiguity plane for each disturbance signal. We want to determine N locations from the 160*160 ambiguity plane, such that values in these locations are very similar for signals from the same class, while they vary significantly for signals from different classes. ~~w, z(i)[q,z]--x( ) [q,z] 1 A4FDK[q,r] = (5) Where, ~(a( [q,zq),=1 dc)[q, z]=+~1 A;;)[q, r] 12-1 Z( ) [q, r] 1 (6) r-1 Here A:) [q, ~] is the value at a particular point in ~ and r on the ambiguity plane corresponding to example signal y, that belongs to class c. ~( )[q, r] is the average value at a particular point for a class i. The weight for distance between class i and class j is ~,,. There are normally two or three classes that are really similar. We specify big weights for their distances to make them easier to discriminate. And cd )[q, T] is the estimated standard deviation at a particular point for the 1 training examples from class c. MFDR is maximized when the separation between means of the class clusters is large, and the within class variance is small, The MFDR designed by a large number of training examples is still a 160*160 plane. We transform it into a binary matrix by replacing the maximum N points with 1 s and the other points with 0 s. By multiplying the binary form of MFDR with certain signal s ambiguity plane, we will find N feature points for this signal. We put them into a vector as its feature vector, as shown in Figure 4 (We take N = 6 here). A. (y+\ ap12 wl,,,-l 43,, wzl q)22..... Wjn-l W2[, ap32.... ap31 ap3,n_, w3... atkl,l apn-,,2 ~ W}l+l+ apn-l,n E, Classification By Neural Network We choose an artificial neural network (ANN) with a 3- layered feedforward structure as the classifier. The inputs of the ANN are features extracted by the scheme presented in the last section. The outputs of the ANN determine which class the disturbance event belongs to. V. NMULATONEXPERMENTS 4= The classification experiments were done with power quality disturbance signals simulated by MatLab. The classes include harmonics, capacitor fast switching transients, capacitor slow switching transients, voltage sudden sag, voltage gradual decay sag, and voltage swell. Figure 4. Apply MFDK onto AP to get feature vector We design and use a Modified Fisher s Discriminant Ratio kernel (MFDR) to get those N locations. Here the MFDR kernel is designed by Z training example signals from each class with the equation as follows, Each example signal consists of five cycles of a voltage waveform sampled 256 times per cycle, with up to 0.570 added randomly generated noise. We use totally 6000 examples (1000 examples per class) to design the modified Fisher s Discriminant Kernel and train the 3-layer feedforward neural network. Totally 1800 examples (300 examples per class) are used to test the classification method. All the example events are generated in a random way. The starting time, duration, and distortion magnitude of each training and testing event are all random. This

makes the testing results more reliable, because none of these are fixed for real power system disturbance events. Substantial computer simulations have been conducted to optimize the feature extraction algorithm and neural network structure. The input layer of ANN has 13 neurons, hidden layer 8 neurons, and output layer 6 neurons. The 13 inputs to the ANN include 12 feature points extracted from the ambiguity plane and one bias. The 6-point output vector determines which category the disturbance event belongs to. The results for a 6-class classification are competitive and shown in table. Class Correct Ml M2 M3 M41M5)M6 (%) (%) (%) (%) (%) (%) (%) l,hm 99-0 1 0 0 0 2,CHST 98 0-2 0 0 0 3.CLST 85.3 0 9.7-4.3 0 0.7 4.VSS 98 0 0 1.3-0.7 0 5.VGD 100 0 0 0 0-0 6,VSW 100 0 0 0 0 0 - Table. Results of a 6-class PQ event classification (Here HM-harmonics, CHST-capacitor high frequency switching transients, CLST-capacitor low frequency switching transients, VSS-voltage sudden sag, VSW-voltage swell; Correctpercentage of correct identification, Mi-percentage of mistake identification to class i,) V. FuzzY CLASSFCATONAPPROACH We have proposed a new classification algorithm for power quality disturbance signals. However, the philosophy behind this algorithm is to take in a disturbance signal and classify the signal to one of the disturbance classes. We call it the crisp classification approach. That means we always assume that there is only one type of disturbance in a 5-cycle waveform. n some cases, however, there are multiple types of disturbances happening at the same time, The crisp classification strategy does not work very well for these combined events, Based on the same feature extraction scheme, we are exploring a fuzzy classification approach that will provide us more accurate, more comprehensive, and more useful information for the power system under our monitoring. The basic idea is as follows. For a given piece of 5-cycle disturbance waveform, we will give a soft evaluation of the disturbance component in this waveform. Specifically, we will determine the grade (i.e. membership function) corresponding to each disturbance class. The grade of an arbitrary class A will show us to what extent the waveform includes the disturbance of class A. For example, if we input a 5-cycle disturbance waveform, we may get an output like this -- Harmonics 2.5, Capacitor fast switching 7.5, Capacitor slow switching 5.5, Sudden sag 0.5, Sag gradual decay 3, Swell 0.5. Thus we know this short duration disturbance is mostly capacitor switching event. But it also has slight harmonics and sag gradual decay components. This strategy serves better the goal of monitoring, analyzing, and evaluating the power quality of a given power system. We are exploring the details of the fuzzy classification idea. Multiple neural networks and statistical signal processing techniques are employed. V. CONCLUSONS dentification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection, A new classification algorithm for power quality disturbances have been proposed and tested in this paper. This algorithm is based on time-frequency ambiguity plane concept, Fisher s Discriminant kernel, and artificial neural network. t shows very high classification performance in the simulation experiments. This novel combination of methods shows promise for future development of fully automated monitoring systems with classification ability. The potential of developing a fuzzy classification method based on this algorithm is also discussed, Power system monitoring becomes more powerful by including the ability of classifying disturbed signals automatically. V. ACKNOWLEDGEMENTS This project is supported by the Advanced Power Technologies (APT) Center at the University of Washington. The APT Center is supported by ALSTOM ESCA, CES, LG ndustrial Systems and Mitsubishi Electric Corp. [1] [2] [3] [4] X. REFERENCES S. Santoso, E. J. Powers, W. M. Grady, A. C. Parsons, Power quality disturbance waveform recognition using wavelet-based neural classifier.. Theoretical foundation, EEE Transactions on Power Delivery, Vol. 15, pp. 222-228, Jan. 2000. B. Perunicic, M. Mallini, Z. Wang, Y. Liu, Power quality disturbance detection and classification using wavelets and artificial neural networks, 8th nternational Conference On Harmonics and Quali~ of Power Proceedings, Vol. 1, pp. 77-82, 1998. A. M. Gaoudaj M, M, A, Salama, M. R, Sultan, A, Y. Chikhani, Power quality detection and classification using wavelet-multiresolution signal decomposition, EEE Transactions On Power Delivery, Vol. 144, pp. 1469-1476, Oct. 1999. J. S. Lee, C. H. Lee, J. O. Kim, S. W. Namj Classification of power quality disturbances using orthogonal polynomial approximation and

bispectra, Electronics Letters, Vol. 33 18, pp. 1522-1524, Aug. 1997. [5] R. C. Dugan, Electrical Power Systems Quality, New York: McGraw-Hill, 1996. [6] D. Mueller, M, McGranaghan, Effects of voltage sags in process industry applications, nct,elcctrotek. corn/pgnet/main/backg rnd/tutorial/sag/paper/paper.htm. [7] F. Hlawatsch, G. F. Boudreaux-Bartels, Linear and quadratic time-frequency signal representations, EEE Signal Processing Magazine, Apr. 1992. [8] J. McLaughlin, J. Droppo, L. Atlas, Classdependent, discrete time-frequency distributions via operator theory, EEE nternational Conference on Acoustics, Speech, and Signal Processing, 1997. [9] B.W. Gillespie, L.E. Atlas, Data-Driven Time- Frequency Classification Techniques Applied To Tool-Wear Monitoring, Proceedings of the 2000 EEE CASSP, 2000. [10] B.W. Gillespie, L.E. Atlas, Optimization of Time and Frequency Resolution for Radar Transmitter dentification, Proceedings of the 1999 EEE CASSP, vol.3, pp. 1341-4, 1999. X. BOGRAPHES Min Wang received his B.S. degree from Tsinghua University, Beijing, China, in 1999. Currently, he is pursuing his M.S. degree in the Department of Electrical Engineering, University of Washington. He also works as a research assistant with Professor Alexander Mamishev. His research and study interests include signal processing, power quality, and software engineering. He is a student member of EEE and the PES. Piotr Ochenkowski received his B.S. and M.S. degrees from the University of Washington in 1999 and 2000, respectively, both in Electrical Engineering. His area of interests included Digital Signal Processing for communications application. He is currently with Boeing Space and Communication Group as an embedded software engineer. Alexander Mamishev received an equivalent of B.S. degree from the Kiev Polytechnic nstitute, Ukraine, in 1992, M.S. degree from Texas A&M University in 1994, and a Ph. D, degree from MT in 1999, all in electrical engineering. Currently he is an Assistant Professor and Director of SEAL (Sensors, Energy, and Automation Laboratory) in the Department of Electrical Engineering, University of Washington, Seattle. Dr. Mamishev is an author of about 40 journal and conference papers, and one book chapter. His research interests include sensor design and integration, dielectrometry, electric power and electrical insulation, electromagnetic, bioengineering, MEMS, optimization, and inverse problem theory. He serves as a reviewer for EEE Transactions on Power Delivery and EEE Transactions on Dielectrics and Electrical nsulation.