S-TRANSFORM-BASED CLASSIFICATION OF POWER QUALITY DISTURBANCE SIGNALS BY SUPPORT VECTOR MACHINES
Yifeng Ding · Proceedings of the CSEE · 2005
Based on Support Vector Machines (SVM) and S-transform, a novel approach to detect and classify various types of electric power quality disturbances is presented. The S-transform is an extension of the continuous wavelet transform and short time Fourier transform, it uses an analysis window whose width is decreasing with frequency and then providing a frequency dependent resolution. For its good time-frequency characteristic, it is suitable for feature extraction of power quality disturbance signals. At first the S-transform is applied to obtain useful features of the non-stationary power quality disturbance signals. Then disturbance types are identified through the pattern recognition classifier based on SVM. Numerical results show that the proposed classification method is an effective technique for building up a pattern recognition system for power network disturbance signals.