European Journal of Molecular & Clinical Medicine

Abstract

With growing use of sensitive equipment, studies on power quality had developed to conduct data analysis on power quality. Wavelet transformation method has been very useful in investigating diverse types of events in power quality. Present paper associates the utilization of different wavelets at various scales and level of disintegration in examining real Power Quality (PQ) occasions from a link model or signal is produced utilizing MATLAB background. In this system voltage sag, swell, harmonics, momentary interruption, fault conditions and transient events are performed. The system proposed includes elements smaller than the traditional procurement process. In this method wavelet transform identified different power quality events and then classified them via Artificial Neural Network (ANN). Power quality disturbances are defined by the load received after neural network training. Separate MATLAB simulation model is designed to produce various power quality events like voltage sag, swell, passing disruption, harmonics, temporary and fault signals. ANN learning also wiped out MATLAB simulation using NN toolbox for power quality disturbance detection through the aliasing of voltage signals energy. Satisfactory results obtained in MATLAB simulink using such techniques.