Abstract
This article presents a new technique for the detection of power quality (PQ) events by using statistically matched wavelet. The statistically matched wavelet is designed based on the characteristics of the PQ event using the concept of fractional Brownian motion. The proposed technique is compared with Daubechies wavelet to show its superiority in the detection of PQ events. To classify the detected events, an iterative closest point algorithm is used which classifies the detected event even in the presence of outlier points and Gaussian noise. The method is applied to classify the various PQ events like transient, sag, swell and harmonics and the results are simulated using MATLAB version 7.3.
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