Obtaining patterns for identification of power quality disturbances using continuous wavelet transform

This paper presents a new approach for obtaining patterns for identification of power quality (PQ) disturbances present in electrical power systems with the use of continuous wavelet transform (CWT). A new difference coefficient matrix (DCM) is proposed, which is calculated from the difference of the CWT coefficients of the pure sinusoidal signal and the PQ disturbance signal. Then, the scale wise sums of coefficients of all the rows of DCM give unique feature matrix (UFM). This paper shows that the UFM posses unique features that can be used to generate the unique patterns of various PQ disturbances. The algorithms of the proposed approach are given together with its implementation on various cases of PQ disturbances namely sag, interruption, swell, transient, harmonics, and flicker with different magnitudes of each of the disturbances. The results show that unique pattern is obtained for each PQ disturbance irrespective of its magnitude, which can be treated as signature of the respective PQ disturbance.