Power Supply Quality Analysis Using S-Transform and SVM Classifier

Jiaqi Li^1*, M. V. Chilukuri²

¹University of Nottingham, Nottingham, UK.
²SMIEEE, University of Nottingham, Semenyih, Malaysia.

DOI: 10.4236/jpee.2014.24059                

Abstract

In this paper, a SVM classifier based on S-Transform is presented for power quality disturbances classification. Firstly, seven types of PQ events are created using Matlab simulation. These signals are analyzed to detect and localize PQ events via S-Transform by visual inspection. Then five significant features of the PQ disturbances are extracted from the S-Transform output. Afterwards, PQ disturbance samples with the five features are fed to SVM for training and automatic classification. Besides, particle swarm optimization is implemented to improve the performance of SVM. The results of the classification indicate that SVM classifier is an effective mechanism to detect and classify power quality disturbances.

Keywords

Power Quality Disturbance; S-Transform; SVM

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Flores, R. (2003) Signal Processing Tools for Power Quality Event Classification.
[2]	Mallat, S. (1989) A Theory for Multiresolution Signal Decomposition: The Wavelet Representation.
[3]	Stockwell, R.G., Mansinha, L. and Lowe, R.P. (1996) Localization of the Complex Spectrum: The S-Transform.
[4]	Vapnik, V. (1998) The Support Vector Method of Function Estimation.
[5]	Batista, J., Afonso, J.L. and Martins, J.S. (2003) Low-Cost Power Quality Monitor Based on a PC.
[6]	Chilukuri, M.V., Dash, P.K. and Basu, K.P. (2004) Time-Frequency Based Pattern Recognition Technique for Detection and Classification of Power Quality Disturbances.
[7]	Vapnik, V.N. (1995) The Nature of Statistical Learning Theory. Springer-Verlag, New York. http://dx.doi.org/10.1007/978-1-4757-2440-0
[8]	Qi, F., Bao, C. and Liu, Y. (2004) A Novel Two-Step SVM Classifier for Voiced/Unvoiced/Silence Classification of Speech.