Emerging time and frequency domain techniques for power quality disturbances analysis

Recognition of power quality events by analyzing voltage waveform disturbances is a very important task for power system monitoring. This paper presents a novel approach for the recognition and classification of power quality disturbances using wavelet transform and support vector machine. The proposed method employs wavelet transform techniques to extract the most important and significant features from details and approximation waves. The obtained severable feature vectors are used for training the support vector machines to classify the power quality disturbances. Five types of disturbances are considered for classification. The simulation results reveal that the combination of wavelet transform and SVM in time and frequency domain can effectively classify different PQ events.