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.