An Efficient Feature Extraction Method Based on Entropy for Power Quality Disturbance

Abstract

This study explores the applicability of entropy defined as thermodynamic state variable introduced by German Physicists Rudolf clausius and also presents the concepts and application of said state variable as a measure of system disorganization. Later an entropy-based feature Analysis method for power quality disturbance analysis has been proposed. Feature extraction of a disturbed power signal provides information that helps to detect the responsible fault for power quality disturbance. A precise and faster feature extraction tool helps power engineers to monitor and maintain power disturbances more efficiently. Firstly, the decomposition coefficients are obtained by applying 10-level wavelet multi resolution analysis to the signals (normal, sag, swell, outage, harmonic and sag with harmonic and swell with harmonic) generated by using the parametric equations. Secondly, a combined feature vector is obtained from standard deviation of these features after distinctive features for each signal are extracted by applying the energy, the Shannon entropy and the log-energy entropy methods to decomposition coefficients. Finally the entropy methods detect the different types of power quality disturbance.