Power Quality Disturbances Classification Using Identity Feature Vector and Support Vector Machine

Correct detection and classification of power quality disturbances is an essential issue in power systems. In this paper, an intelligent method is proposed to detect the power quality disturbances. This method is based on an identity vector framework that produces a fixed-length vector for each perturbation. In the first step of the proposed pipeline, the discrete wavelet transform is used to analyze power quality events and extract the features of each input signal, and then the identity vector is built using the approximation coefficients. After applying some normalization on the obtained identity, it is classified using a support vector machine classifier. In order to evaluate the proposed method, twelve types of disturbances have been synthesized and the efficiency of the proposed system is investigated using them. In addition, to verify the robustness of the proposed approach towards the noise, the synthesized signals are contaminated with white gaussian noise with different SNR values, 30 dB, 40 dB and 50 dB. The results of the experiments demonstrate the efficiency of the proposed method for the classification of power quality signals with an accuracy of 99.2%.