Detection and Classification of Power Quality Disturbances Using Wavelet Transform and Support Vector Machines

Abstract

Recognition of power quality events by analyzing voltage waveform disturbances is a very important task for power system monitoring. This article presents a novel approach for the recognition and classification of power quality disturbances using wavelet transform and wavelet-support vector machines. The proposed method employs wavelet transform techniques to extract the most important and significant feature from details and approximation waves. The obtained severable feature vectors are used for training the support vector machines to classify the power quality disturbances. Various transient events, such as voltage sag, swell, interruption, harmonic, transient, sag with harmonic, swell with harmonic, and flicker, are tested. Sensitivity of the proposed algorithm under different noise conditions is investigated in this article. The results show that the classifier can detect and classify different power quality signals, even under noisy conditions, correctly.