Analysis through mathematical model of power quality disturbances to improvement

Abstract

Power quality studies have developed to analyses power quality data as the use of delicate electronic equipment has increased. The wavelet transformation method was developed to be more useful for analyzing different kinds of power quality events. In this research, we analyses the utilization of several wavelet types at various sizes and decomposition level of the system analyzing actual value of the power quality disturbances events in the transmission system. Signal created using MATLAB backdrop. Testing has been done on voltage sag, voltage swell, and transient events. This technique makes use of Wavelet transform and Artificial Neural Network (ANN) to identify and categories power quality disturbances in the power system. Comparing the suggested method to the standard approach, fewer characteristics are needed for identification. Artificial neural networks are taught to classify events using the feature that the wavelet extracts. The weight gained after neural network training is used to categories PQ concerns between the quantity demand and the price.