A Neural Network based power quality signal classification system using wavelet energy distribution

This paper presents a method for the classification of common Power Quality(PQ) events. The described system for the characterization of disturbances is based on wavelet based feature extraction. The amount of data to be analyzed and how the data can be interpreted are of crucial importance in power quality analysis. Wavelet Transform(WT) has been widely used in power quality signal analysis. The advantage of wavelet transform is it can provide precise time information of power quality events and has many advantages over traditional signal analysis approaches. In this paper Discrete Wavelet Transform(DWT) is used for obtaining the energy distribution from simulated signals. The system is developed with Neural Network which is an effective tool in classification of signals in power systems.