IDENTIFICATION AND CLASSIFICATION OF POWER QUALITY DISTURBANCES BY AN EFFECTIVE WAVELET BASED NEURAL CLASSIFIER

This paper presents a wavelet based wavelet based neural for identification and classification of Power Quality disturbances. The disturbances to be classified from the power system under study and it is simulated in Power System Computer Aided Design (PSCAD).In this paper, the Power Quality disturbances to be identified and classified with the help of Discrete Wavelet Transform and Artificial Neural Network. Power Quality disturbances will be detected by using Discrete Wavelet Transform (DWT) and classified by using Artificial Neural Network (ANN). Discrete Wavelet Transform is used to extract the disturbance features in the power signal. Power quality disturbances are localized by Discrete Wavelet Transform in in time and frequency domain. Multi-Resolution Analysis (MRA) technique of Discrete Wavelet Transform is used to decompose the power signal at 7-level which gives detailed and approximate coefficients. These detailed coefficients of DWT are used to calculate Statistical parameters given as input to ANN to classify Power quality disturbances