Classification of Power Quality Disturbances Using Wavelet Packet Energy Entropy and LS-SVM

Ming Zhang, Kaicheng Li, Yisheng Hu

.

DOI: 10.4236/epe.2010.23023                

Abstract

The power quality (PQ) signals are traditionally analyzed in the time-domain by skilled engineers. However, PQ disturbances may not always be obvious in the original time-domain signal. Fourier analysis transforms signals into frequency domain, but has the disadvantage that time characteristics will become unobvious. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. In this paper, there were two stages in analyzing PQ signals: feature extraction and disturbances classification. To extract features from PQ signals, wavelet packet transform (WPT) was first applied and feature vectors were constructed from wavelet packet log-energy entropy of different nodes. Least square support vector machines (LS-SVM) was applied to these feature vectors to classify PQ disturbances. Simulation results show that the proposed method possesses high recognition rate, so it is suitable to the monitoring and classifying system for PQ disturbances.

Keywords

Power Quality (PQ), Wavelet Packet Transform (WPT), Wavelet Packet Log-Energy Entropy, Least Square Support Vector Machines (LS-SVM)

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	S. Mallat, “A Wavelet Tour of Signal Processing,” Academic Press, San Diego, California, 1998.
[2]	S. Santoso, E. J .Powers and P. Hofman, “Power Quality Assessment via Wavelet Transform Analysis,” IEEE Transaction on Power Delivery, Vol. 11, No. 2, 1996, pp. 924-930.
[3]	H. T. Yang and C. C. Liao, “A De-noising Scheme for Enhancing Wavelet-based Power Quality Monitoring System,” IEEE Transaction on Power Delivery, Vol. 16, No. 3, 2001, pp. 353-360.
[4]	S. Santoso, E. J. Powers and W. M. Grady, “Power Quality Disturbance Data Compression Using Wavelet Transform Methods,” IEEE Transaction on Power Delivery, Vol. 12, No. 3, 1997, pp. 1250-1257.
[5]	A. K. Ghosh and D. L. Lubkeman, “The Classification of Power System Disturbance Waveforms Using a Neural Network Approach,” IEEE Transaction on Power Delivery, Vol. 10, No. 1, 1995, pp. 109-115.
[6]	T. X. Zhu, S. K. Tso and K. L. Lo, “Wavelet-based Fuzzy Reasoning Approach to Power Quality Disturbance Recognition,” IEEE Transaction on Power Delivery, Vol. 19, No. 4, 2004, pp. 1928-1935.
[7]	M. B. I. Reaz, F. Choong, M. S. Sulaiman, F. Mohd- Yasin and M. Kamada, “Expert System for Power Quality Disturbance Classifier,” IEEE Transaction on Power Delivery, Vol. 22,No. 3, 2007, pp. 1979-1988.
[8]	P. Janik and T. Lobos, “Automated Classification of Power Quality Disturbances Using SVM and RBF Networks,” IEEE Transaction on Power Delivery, Vol. 21, No. 3, 2006, pp. 1663-1669.
[9]	J. Chung, E. J. Powers, W. M. Grady and S. C. Bhatt, “Power Disturbance Classi?er Using a Rule-Based Method and Wavelet Packet-Based Hidden Markov Model,” IEEE Transaction on Power Delivery, Vol. 17, No. 1, 2002, pp. 233-241.
[10]	IEEE Recommended Practice for Monitoring Electric Power Quality, IEEE Standards Description: 1159-1995, 2009.
[11]	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. 1-2, 2008, pp. 143-149.
[12]	J. A. K. Suykens and J. Vandewalle, “Least Squares Support Vector Machine Classifiers”, Neural Processing Letter, Vol. 9, No. 3, 1999, pp. 293-300.
[13]	N. S. D. Brito, B. A. Souza and F. A. C. Pires, “Daubechies Wavelets in Quality of Electrical Power,” 8th International Conference on Harmonics and Quality of Power, Athens, 14-18 October 1998, pp. 511-515.
[14]	T. K. Abdel-Galil, M. Kamel, A. M. Youssef, E. F. El-Saadany and M. M. A. Salama, “Power Quality Disturbance Classification Using the Inductive Inference Approach,” IEEE Transaction on Power Delivery, Vol. 19, No. 4, 2004, pp. 1812-1818.