Identification of partial discharge location using probabilistic neural networks and the fuzzy c-means clustering approach

Ju Chu Hsieh, Cheng Chi Tai, Ming Shou Su, Yu Hsun Lin

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

This article proposes an approach for location of partial discharge sources in the power cable and gas-insulated load break switches using a probabilistic neural networks and the fuzzy C-means clustering approach. Three different defect positions are designed in the power cable and gas-insulated load break switches. The three different defect positions of partial discharge occurrence are located by the proposed method. Discrete wavelet transform is employed to suppress noises of measured signals by the high-frequency current transformer. The proposed method can assist electrical engineers in making accurate statistical judgments. To accurately discover the different defect positions, the proposed method uses feature extraction and statistical analysis of the measured signals. Finally, experimental results validate that the proposed approach can effectively determine the location of partial discharge sources in practical partial discharge measurement.

Original languageEnglish
Pages (from-to)60-69
Number of pages10
JournalElectric Power Components and Systems
Volume42
Issue number1
DOIs
Publication statusPublished - 2014 Jan 2

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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