RMS percent of wavelet transform for the detection of stochastic high impedance faults

T. M. Lai, Wai Chau Edward Lo, W. M. To, K. H. Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

High impedance faults (HIF) are faults which are difficult to detect by overcurrent protection relays. This paper presents a practical pattern recognition based algorithm for electric distribution high impedance fault detection. The scheme recognizes the distortion of the voltage and current waveforms caused by the arcs usually associated with HIF. The analysis using rms ratios of Discrete Wavelet Transform (DWT) yields three phase voltage and current in the low frequency range which are fed to a classifier for pattern recognition. The classifier is based on the algorithm using artificial neural network (ANN) approach. A HIF model was also developed, where the random nature of the arc was simulated using MATLAB.
Original languageEnglish
Title of host publicationProceedings of 2012 IEEE 15th International Conference on Harmonics and Quality of Power, ICHQP 2012
Pages823-828
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2012
Event2012 IEEE 15th International Conference on Harmonics and Quality of Power, ICHQP 2012 - Hong Kong, Hong Kong
Duration: 17 Jun 201220 Jun 2012

Conference

Conference2012 IEEE 15th International Conference on Harmonics and Quality of Power, ICHQP 2012
CountryHong Kong
CityHong Kong
Period17/06/1220/06/12

Keywords

  • High Impedance Faults
  • Pattern Recognition
  • Wavelet Transforms

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Energy Engineering and Power Technology
  • Fuel Technology
  • Electrical and Electronic Engineering

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