Time-frequency analysis of power-quality disturbances via the Gabor-Wigner transform

Soo Hwan Cho, Gilsoo Jang, Sae Hyuk Kwon

Research output: Contribution to journalArticlepeer-review

152 Citations (Scopus)

Abstract

Recently, many signal-processing techniques, such as fast Fourier transform, short-time Fourier transform, wavelet transform (WT), and wavelet packet transform (WPT), have been applied to detect, identify, and classify power-quality (PQ) disturbances. For research on PQ analysis, it is critical to apply the appropriate signal-processing techniques to solve PQ problems. In this paper, a new time-frequency analysis method, namely, the Gabor-Wigner transform (GWT), is introduced and applied to detect and identify PQ disturbances. Since GWT is an operational combination of the Gabor transform (GT) and the Wigner distribution function (WDF), it can overcome the disadvantages of both. GWT has two advantages which are that it has fewer cross-term problems than the WDF and higher clarity than the GT. Studies are presented which verify that the merits of GWT make it adequate for PQ analysis. In the case studies considered here, the various PQ disturbances, including voltage swell, voltage sag, harmonics, interharmonics, transients, voltage changes with multiple frequencies and voltage fluctuation, or flicker, will be thoroughly investigated by using this new time-frequency analysis method.

Original languageEnglish
Article number5352305
Pages (from-to)494-499
Number of pages6
JournalIEEE Transactions on Power Delivery
Volume25
Issue number1
DOIs
Publication statusPublished - 2010 Jan

Keywords

  • Gabor transform (GT)
  • Gabor-Wigner transform (GWT)
  • Power-quality (PQ) analysis
  • Time-frequency analysis
  • Wigner distribution function (WDF)

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Time-frequency analysis of power-quality disturbances via the Gabor-Wigner transform'. Together they form a unique fingerprint.

Cite this