Adjusted Haar wavelet for application in the power systems disturbance analysis

Abhisek Ukil, Rastko Živanović

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

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.

Original languageEnglish
Article number2
Pages (from-to)103-115
Number of pages13
JournalDigital Signal Processing: A Review Journal
Volume18
Issue number2
DOIs
Publication statusPublished - Mar 2008
Externally publishedYes

Keywords

  • Abrupt change detection
  • Adjusted Haar wavelet
  • Power systems disturbance analysis
  • Signal segmentation

Fingerprint Dive into the research topics of 'Adjusted Haar wavelet for application in the power systems disturbance analysis'. Together they form a unique fingerprint.

Cite this