High-impedance fault detection in the distribution network using the time-frequency-based algorithm

Amin Ghaderi, Hossein Ali Mohammadpour, Herbert L. Ginn, Yong June Shin

Research output: Contribution to journalArticle

93 Citations (Scopus)

Abstract

A new high-impedance fault (HIF) detection method using time-frequency analysis for feature extraction is proposed. A pattern classifier is trained whose feature set consists of current waveform energy and normalized joint time-frequency moments. The proposed method shows high efficacy in all of the detection criteria defined in this paper. The method is verified using real-world data, acquired from HIF tests on three different materials (concrete, grass, and tree branch) and under two different conditions (wet and dry). Several nonfault events, which often confuse HIF detection systems, were simulated, such as capacitor switching, transformer inrush current, nonlinear loads, and power-electronics sources. A new set of criteria for fault detection is proposed. Using these criteria, the proposed method is evaluated and its performance is compared with the existing methods. These criteria are accuracy, dependability, security, safety, sensibility, cost, objectivity, completeness, and speed. The proposed method is compared with the existing methods, and it is shown to be more reliable and efficient than its existing counterparts. The effect of choice of the pattern classifier on method efficacy is also investigated.

Original languageEnglish
Article number6915897
Pages (from-to)1260-1268
Number of pages9
JournalIEEE Transactions on Power Delivery
Volume30
Issue number3
DOIs
Publication statusPublished - 2015 Jun 1

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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