Preparation of Papers in a Two-Column Format

This paper presents an efficient feature extraction method based on discrete wavelet transform for the classification of the power quality disturbances. Firstly, the decomposition coefficients are obtained by applying 10-level wavelet multi resolution analysis to the signals (normal, sag, swell, outage, harmonic, 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 support vector machine (SVM) classifier is used for classification performance of proposed feature extraction method. The regularization parameter and kernel parameter of the SVM are determined by 10-fold cross validation. Simulation results indicate that the combined feature vector has more high classification accuracy with regard to the other feature vectors.