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Abrupt change detection based on the wavelet transform and threshold methodis very effective in detecting the abrupt changes and hence segmenting thesignals recorded during disturbances in the electrical power network. Thewavelet method estimates the time-instants of the changes in the signal modelparameters during the pre-fault condition, after initiation of fault, aftercircuit-breaker opening and auto-reclosure. Certain kinds of disturbancesignals do not show distinct abrupt changes in the signal parameters. In thosecases, the standard mother wavelets fail to achieve correct event-specificsegmentations. A new adjustment technique to the standard Haar wavelet isproposed in this paper, by introducing 2n adjusting zeros in the Haar waveletscaling filter, n being a positive integer. This technique is quite effectivein segmenting those fault signals into pre- and post-fault segments, and it isan improvement over the standard mother wavelets for this application. Thispaper presents many practical examples where recorded signals from the powernetwork in South Africa have been used.

Adjusted Haar Wavelet for Application in the Power Systems Disturbance Analysis