Wavelet transform based power quality events classification using artificial neural network and SVM

Abstract

This paper demonstrates classification of PQ events utilizing wavelet transform (WT) energy features by artificial neural network (ANN) and SVM classifiers. The proposed scheme utilizes wavelet based feature extraction to be used for the artificial neural networks in the classification. Six different PQ events are considered in this study. Three types of neural network classifiers such as feed forward multilayer back propagation (FFML), learning vector quantization (LVQ), probabilistic neural network (PNN) are analyzed for effective classification of PQ events. The results show the superiority of PNN over FFML and LVQ. The test simulations show that SVM has higher performance than ANN with feed forward multilayer back propagation (FFML), learning vector quantization (LVQ), probabilistic neural network (PNN).

Keywords: Power Quality, Daubechies, Multi Resolution Analysis, Feature Extraction, Neural networks, SVM