Detection and classification of power quality events using DWT and MSD

In power system the electrical signals always have some disturbances and discontinuities. The advanced technological world demands electric power which is free from any disturbance. Many of these problems such as equipment failure, and low response of equipment are the results of the disruption of the electric power supply. Computer based processes or automatic system fails to operate due to these power disturbances. Hence, detection and classification of these power quality events is necessary to avoid such incidents. In this paper, nine classes of power quality (PQ) events are detected by applying discrete wavelet transform (DWT) and multi-resolution signal decomposition (MSD). DWT coefficients based approach for the energy contents in the different frequency zone is proposed for the classification of PQ disturbances. The coefficients at each levels are used for extracting the features of various disturbances. Classification is done on the basis of DWT and MSD with these extracted features. Features extracted using DWT coefficients have important role for classifying different PQ disturbances. Several simulations for detection and classification of these PQ events based on MSD and DWT are performed. Classification of all nine events is shown to demonstrate the effectiveness of proposed method.