A Practical Approach to Detection and Analysis of Time-Varying Waveform

Abstract

The power quality has attracted considerable attention from both utilities and users due to the development of sensitive electronic equipment in power system network. A novel method for detecting and localizing power quality disturbance based on wavelet neural network is presented. The wavelet transform is established on decomposition of a signal according to time-scale using basis functions with adaptable scaling properties known as multiresolution analysis. The wavelet transform expands a signal not in terms of a trigonometric polynomial but by wavelets, generated using transition and dilation of a fixed wavelet function. The feature obtained from wavelet transform coefficients are inputted into the neural network for power quality disturbance pattern recognition. The improved network algorithm is utilized to complete the network structure initialization. The fundamental component of the signal is estimated to extract the mixed information using wavelet network, and then the disturbance is acquired by subtracting the fundamental component. The simulation results show that the proposed method is more sensitive to signal singularity, and achieves better performance over traditional method.