A New Method for Detection and Classification of Power Quality Events Using Discrete Wavelet Transform and Correlation Coefficients

This paper presents a novel and simple approach to detecting and classifying a wide range of power quality (PQ) events based on the discrete wavelet transform (DWT) and correlation coefficient. For this purpose, two new indices are proposed and the type of PQ event is detected by comparing the values of the correlation coefficient between the value of these indices for the pre-stored PQ events and for a recorded indistinct signal. This algorithm enjoys the advantages of DWT and correlation coefficient and it does not suffer the disadvantages of neural networks or neural network-fuzzy based algorithms such as training and high dimension input matrices or the disadvantages of Fourier transform-based approaches such as unsuitability for non-stationary signals as it does not track signal dynamics properly due to the limitation of fixed window width. The effectiveness of the method tested by numerous PQ disturbance and simulation results confirms the competency and the ability of the proposed method in detection and automatic diagnosis of PQ disturbances. Compared with the other methods, the simulation under different noise conditions verifies the effectiveness of the noise immunity and the relatively better accuracy of the proposed method.