Adjusted Haar Wavelet for Application in the Power Systems Disturbance Analysis

TL;DR

This paper introduces an adjusted Haar wavelet technique with added zeros to improve segmentation of power system disturbance signals, especially those lacking clear abrupt changes, demonstrated through practical South African power network data.

Contribution

A novel adjustment method for the Haar wavelet using 2n zeros in the scaling filter enhances signal segmentation in power disturbance analysis.

Findings

Improved segmentation accuracy for signals without distinct abrupt changes.

Effective application demonstrated on real South African power network data.

Outperforms standard wavelets in fault signal segmentation.

Abstract

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