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In this paper, the lifting based second generation Wavelet Transform (SGWT) is implemented along with the widely used discrete Wavelet Transform (DWT) for the detection and localization of ten different types of power quality (PQ) disturbance signals. The SGWT provides the time domain interpretation which is an opposition to the frequency domain analysis of DWT. Further, the selected features are extracted from the detail coefficient of the variants of WT and given as inputs to the classifiers in order to characterize the signals. Moreover, a comparative assessment of the PQ signal carried out with different classifiers such as Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) have been presented along with aforementioned detection techniques. The RF is an ensemble decision tree and used for the classification of large number of data set. Hence, various single as well as combined power quality disturbance signals have been simulated in noisy and noise free environment in order to demonstrate the efficiency of the proposed techniques. Moreover, in order to represent in realistic environment, these proposed techniques are tested with both the single phase and tree phase signals captured from different transmission panels. Further, to aid this PQ disturbance detection, different types of real time fault signals are characterized with these aforementioned approaches.

Fast Methods for Power Quality Analysis