Detection and classification of power quality events based on wavelet transform and artificial neural networks for smart grids

Abstract

In this paper, A powerful signal processing method wavelet transform is presented to detect power quality events among one of the Artificial intelligence techniques which is Artificial neural networks as a classification system. As a result of the increased applications of non-linear load, it becomes important to find accurate detecting method. Wavelet Transform represents an efficient signal processing algorithm for power quality problems especially at non-stationary situations. These events are generated and filtered using wavelet as well as extraction of their features at different frequencies. Thereafter, a training process is done using ANN to classify power quality events.