Classification of power quality disturbances based on S-transform and image processing techniques

This paper presents a method that combines discrete S-transform (DST) time-frequency distribution (TFD) and local binary pattern (LBP) based image analysis technique for classifying power quality (PQ) disturbances. The purpose of this combination is to extract discriminative features by utilizing from both capability of generating the compact TFD of a non-stationary signal and the efficient image representation capability of LBP. In the proposed method, DST based TFDs of PQ disturbance signals are considered as 2-D images. LBP histograms are used to extract the features from TF images. Initially, the uniform patterns in TF images are obtained by the LBP operator. Next, the occurrence histograms of these patterns are used to produce representative feature vectors that can capture the unique and salient characteristics of each disturbance. The classification performance of the proposed method is evaluated through total 2400 disturbance signals. The experimental results have shown that the rate of correct classification is about 98 % for the different PQ disturbances.

[1]  Zhongxing Geng,et al.  The algorithm of interpolating windowed FFT for harmonic analysis of electric power system , 2001 .

[2]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[3]  M. Uyar,et al.  An effective wavelet-based feature extraction method for classification of power quality disturbance signals , 2008 .

[4]  H. He,et al.  A self-organizing learning array system for power quality classification based on wavelet transform , 2006, IEEE Transactions on Power Delivery.

[5]  M. Ozdemir,et al.  Comparison of statistical methods and wavelet energy coefficients for determining two common PQ disturbances: Sag and swell , 2009, 2009 International Conference on Electrical and Electronics Engineering - ELECO 2009.

[6]  P.K. Dash,et al.  Multiresolution S-transform-based fuzzy recognition system for power quality events , 2004, IEEE Transactions on Power Delivery.

[7]  Okan Ozgonenel,et al.  A hybrid approach for power quality monitoring , 2012 .

[8]  E.F. El-Saadany,et al.  Power quality disturbance classification using the inductive inference approach , 2004, IEEE Transactions on Power Delivery.

[9]  Ozgul Salor,et al.  Feature construction, selection and revealing patterns of power quality event data , 2010, 2010 IEEE 18th Signal Processing and Communications Applications Conference.

[10]  Muhsin Tunay Gençoglu,et al.  An expert system based on S-transform and neural network for automatic classification of power quality disturbances , 2009, Expert Syst. Appl..

[11]  V. Nabiyev,et al.  LBP Yardımıyla Görüntüdeki Kişinin Yaşının Bulunması , 2011 .

[12]  Cheng Wang,et al.  A novel extended local-binary-pattern operator for texture analysis , 2008, Inf. Sci..

[13]  Zhao Rong,et al.  FFT Algorithm with High Accuracy for Harmonic Analysis in Power System , 2006 .

[14]  Zahra Moravej,et al.  New Combined S-transform and Logistic Model Tree Technique for Recognition and Classification of Power Quality Disturbances , 2011 .

[15]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..