Abstract
Wavelet transforms have fundamentally been used to detect the major features of power quality events, as the transforms adapt to dynamic signals and are appropriate for capturing time-localised, short-period phenomena. The multiwavelets technique is proposed here to classify power quality events. This leads to easy extraction of a quality feature set, which is further used for classification and decision making. The proposed classification has three stages. The events detected from the test data are in accordance with the IEEE standards first stage. Two subclassifiers with different confidence levels have been used, along with the Dempster-Shafer classifier, which works as the decision-maker. The two subclassifiers are the chi-square distribution and Heuristic classifier.
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