A Generalized Empirical Wavelet Transform for Classification of Power Quality Disturbances

This paper proposes a generalized empirical wavelet transform (GEWT) for the recognition of single and combined power quality (PQ) disturbances. The FFT based frequency estimation is adaptive, requires no prior information and is also capable to diagnose all the PQ disturbances. The improved spectral segmentation followed by an adaptive filter design accurately extracts the fundamental frequency component, thereby enabling the extraction of informative features. Thus, the proposed approach combines the GEWT and a simple rule based decision tree (DT) for accurate recognition of most significant PQ disturbances. The DT classifier with five features extracted from the GEWT is refined and finally tested on 1200 simulated as well as three real disturbance signals. The proposed scheme is found to be computationally efficient and performs satisfactorily with a good classification accuracy.