A New Scheme for Automatic Classification of Power Quality Disturbances Based on Signal Processing and Machine Learning
Subject Areas : electrical and computer engineeringAbstract :
Identification and classification of power quality disturbances (PQDs) are one of the most important functions of monitoring and protection of modern power systems. One of the most important issues in PQ analysis is automatic diagnosis of waveforms using an effective algorithm. This paper presents an effective method, for extracting features, using integration of discrete wavelet transform (DWT) and hyperbolic S transform (HST). Moreover, an efficient feature selection method namely Orthogonal Forward Selection (OFS) by incorporating Gram Schmidt (GS) procedure and forward selection is applied for selection of the best subset features. Multi support vector machines (MSVM), as famous classifier, is applied. Also, the variable parameters of the classifier are optimized using a powerful method namely particle swarm optimization (PSO). Six single disturbances and two complex disturbances as well pure sine (normal) selected as reference are considered for the classification. Sensitivity of the proposed expert system under different noisy conditions is investigated. Also, efficiency of the proposed methods by comparing the results of this study with the results of other papers is examined.
References:
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[1] D. Granados-Lieberman, R. J. Romero-Troncoso, R. A. Osornio-Rios, A. Garcia-Perez, and E. Cabal-Yepez, "Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review," IET Generation, Transmission, and Distribution, vol. 5, no. 4, pp. 519-529, Apr 2010.
[2] M. Kumar Saini and R. Kapoor, "Classification of power quality events - a review," Electrical Power and Energy Systems, vol. 43, no. 1, pp. 11-19, Dec. 2012.
[3] H. Eristi and Y. Demir, "A new algorithm for automatic classification of power quality events based on wavelet transform and SVM," Expert Systems with Applications, vol. 37, no. 6, pp. 4094-4102, Jun. 2010.
[4] J. G. M. S. Decanini, M. S. Tonelli-Neto, F. C. V. Malange, and C. R. Minussi, "Detection and classification of voltage disturbances using a fuzzy-ARTMAP-wavelet network," Electric Power Systems Research, vol. 81, no. 12, pp. 2057-2065, Dec. 2011.
[5] M. Uyar, S. Yildirim, and M. T. Gencoglu, "An expert system based on S-transform and neural network for automatic classification of power quality disturbances," Expert Systems with Applications, vol. 36, no. 3, pp. 5962-5975, Apr. 2009.
[6] H. S. Behera, P. K. Dash, and B. Biswal, "Power quality time series data mining using S-transform and fuzzy expert system," Applied Soft Computing, vol. 10, no. 3, pp. 945-955, Jun. 2010.
[7] B. K. Panigrahi, P. K. Dash, and J. B. V. Redd, "Hybrid signal processing and machine intelligence techniques for detection, quantification, and classification of power quality disturbances," Engineering Applications of Artificial Intelligence, vol. 22, no. 3, pp. 442-454, Apr. 2009.
[8] M. V. Chilukuri and P. K. Dash, "Multiresolution S-transform-based fuzzy recognition system for power quality events," IEEE Trans. on Power Delivery, vol. 19, no. 1, pp. 323-330, Jan. 2004.
[9] M. Uyar, S. Yildirim, and M. T. Gencoglu, "An effective wavelet-based feature extraction method for classification of power quality disturbance signals," Electric Power Systems Research, vol. 78, no. 10, pp. 1747-1755, Oct. 2008.
[10] H. Eris, A. Ucar, and Y. Demir, "Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines," Electric Power Systems Research, vol. 80, no. 7, pp. 743-752, Jul. 2010.
[11] S. Gunal, O. N. Gerek, D. Gokhan Ece, and R. Edizkan, "The search for optimal feature set in power quality event classification," Expert Systems with Applications, vol. 36, no. 7, pp. 10266-10273, Sep. 2009.
[12] P. K. Dash and S. R. Samantaray, "A novel distance protection scheme using time-frequency analysis and pattern recognition approach," Electrical Power and Energy Systems, vol. 29, no. 2, pp. 129-137, Feb. 2007.
[13] M. Kezunovic and Y. Liao, "A novel software implementation concept for power quality study," IEEE Trans. on Power Delivery, vol. 17, no. 2, pp. 544-549, Apr. 2002.
[14] Y. Liao and J. B. Lee, "A fuzzy - expert system for classifying power quality disturbances," International J. Electrical Power and Energy System, vol. 26, no. 3, pp. 199-205, Mar 2004.
[15] A. Thapar, T. K. Saha, and Z. Y. Dong, "Investigation of power quality categorization and simulating its impact on sensitive electronic equipment," Power Engineering Society General Meeting, vol. 6, no. 10, pp. 528-533, Jun. 2004.
[16] B. Bizjak and P. Planinsic, "Classification of power disturbances using fuzzy logic," in International Conf. on Power Electronics and Motion Control, vol. 1, pp. 1356-1360, Aug./Sep. 2006.
[17] G. S. Hu, J. Xie, and F. F. Zhu, "Classification of power quality disturbances using wavelet and fuzzy support vector machines," in Proc. Int. Conf. on Machine Learning and Cybernetics, vol. 7, pp. 3981-3984, 18-21 Aug. 2005.
[18] R. Hooshmand and A. Enshaee, "Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm," Electric Power Systems Research, vol. 80, no. 12, pp. 1552-1561, Dec. 2010.
[19] T. Nguyen and Y. Liao, "Power quality disturbance classification utilizing S-transform and binary feature matrix method," Electric Power Systems Research, vol. 79, no. 4, pp. 569-575, Apr. 2009.
[20] Z. He, S. Gao, X. Chen, J. Zhang, Z. Bo, and Q. Qian, "Study of a new method for power system transients classification based on wavelet entropy and neural network," Electrical Power and Energy Systems, vol. 33, no. 3, pp. 402-410, Mar. 2011.
[21] S. Kaewarsa, K. Attakitmongcol, and T. Kulworawanichpong, "Recognition of power quality events by using multi wavelet-based neural networks," Electrical Power and Energy Systems, vol. 30, no. 4, pp. 254-260, May 2008.
[22] G. S. Hu, F. F. Zhu, and Z. Ren, "Power quality disturbance identification using wavelet packet energy entropy and weighted support vector machines," Expert Systems with Applications, vol. 35, no. 2, pp. 143-149, Aug. 2008.
[23] C. N. Bhende, S. Mishra, and B. K. Panigrahi, "Detection and classification of power quality disturbances using S-transform and modular neural network," Electric Power Systems Research, vol. 78, no. 1, pp. 122-128, Jan. 2008.
[24] H. T. Yang and C. C. Liao, "A de-noising scheme for enhancing wavelet-based power quality monitoring system," IEEE Trans. on Power Delivery, vol. 16, no. 3, pp. 353-360, Jul. 2001.
[25] C. W. Lu and S. J. Huang, "An application of B-spline wavelet transform for notch detection enhancement," IEEE Trans. on Power Delivery, vol. 19, no. 3, pp. 1419-1425, Jul. 2004.
[26] H. Eristi, A. Ucar, and Y. Demir, "Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines," Electric Power Systems Research, vol. 80, no. 7, pp. 743-752, Jul. 2010.
[27] Z. Moravej, M. Pazoki, and A. A. Abdoos, "Wavelet transform and multi-class relevance vector machines based recognition and classification of power quality disturbances," Euro Trans. on Electrical Power, vol. 21, no. 1, pp. 221-222, Jan. 2010.
[28] V. Fernao Pires, T. G. Amaral, and J. F. Martins, "Power quality disturbances classification using the 3-D space representation and PCA based neuro-fuzzy approach," Expert Systems with Applications, vol. 38, no. 9, pp. 11911-11917, Sep. 2011.
[29] Y. Sun, S. Todorovic, and S. Goodison, "Local - learning - based feature selection for high - dimensional data analysis," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1610-1626, Sep. 2010.
[30] S. Foithong, O. Pinngern, and B. Attachoo, "Feature subset selection wrapper based on mutual information and rough sets," Expert Systems with Applications, vol. 39, no. 1, pp. 574-584, Jan. 2012.
[31] J. Li, M. T. Manry, P. L. Narasimha, and C. Yu, "Feature selection using a piecewise linear network," IEEE Trans. on Neural Networks, vol. 17, no. 5, pp. 1101-1105, Sep. 2006.
[32] K. Z. Mao, "Orthogonal forward selection and backward elimination algorithms for feature subset selection," IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 34, no. 1, pp. 629-634, Feb. 2004.
[33] K. Manimala, K. Selvi, and R. Ahila, "Hybrid soft computing techniques for feature selection and parameter optimization in power quality data mining," Applied Soft Computing, vol. 1111, no.8, pp. 5485–5497, Dec. 2011.
[34] M. Hari Krishnan and R. Viswanathan, "New concept of reduction of gaussian noise in images based on fuzzy logic," Applied Mathematical Sciences, vol. 7, no. 12, pp. 595-602, Sep. 2013.
[35] www.sutech.ac.ir/portal/channels/fckuploadedfiles/fa/743/Documents.
[36] S. Verdu, "Spectral efficiency in the wideband regime," IEEE Trans. on Information Theory, vol. 48, no. 6, pp. 132-140, Jun. 2002.
[37] H. Zhang, P. Liu, and O. P. Malik, "Detection and classification of power quality disturbances in noisy conditions," in IEE Proc. Gen Transm Distrib, vol. 150, no. 5, pp. 567-572, Sep. 2003.
[38] P. O'shea, "A high - resolution spectral analysis algorithm for power - system disturbance monitoring," IEEE Trans. on Power Systems, vol. 17, no. 3, pp. 676-680, Jun. 2002.
[39] F. Zhao and R. Yang, "Power - quality disturbance recognition using S transform," IEEE Trans. on Power Delivery, vol. 22, no. 2, pp. 944-950, Apr. 2007.