A New Approach to Recognize Power Quality Disturbances Based on Wavelet Transform and BP Neural Network

Classification of power quality disturbances consists of two stages,namely,characteristic vector extraction and classifier construction.In most classification methods based on wavelet transform and neural network the energy distribution in each layer obtained by wavelet decomposition is used as the characteristic vector of the layer,and the classification results is given by neural network,however the performances of the clasifier constructed by such methods are to be further improved.In this paper,a set of wavelet transform based characteristic vectors are taken as the inputs of the classifier.By means of the strategy based on least square method the outputs of mutually independent neural networks are synthesized to achieve final recognition result.Results of calculation example show that the proposed classifier is accurate and under white noise background with the S/N ratio of 20db the obtainable reconnition accuracy is 93.18%.The proposed classifier can effectively recognize six typical power quality disturbance patterns: voltage interruption,voltage sag,voltage swell,harmonics,oscillating transient and flicker.