Identification of partial discharge location in a power cable using fuzzy inference system and probabilistic neural networks

Ming Shou Su, Jiann Fuh Chen, Yu Hsun Lin, Chih Kun Cheng

研究成果: Article同行評審

12 引文 斯高帕斯(Scopus)

摘要

This article proposes an approach for identification of partial discharge location in a power cable using a fuzzy inference system and probabilistic neural networks. For accurate determination of the partial discharge source, feature extraction of the measured signals is used in the proposed method. White Gaussian noise, which simulates the high noise environment, is added to the partial discharge signals when making measurements. The accurate ratios of the partial discharge occurrence prediction using conventional observations of partial discharge signals via oscilloscopes are much improved by the proposed method. According to the concept of power delivery, both the peak absolute value and average power of partial discharge signals are adopted as input variables of the fuzzy inference system and probabilistic neural networks. Finally, experimental results validate that the proposed approach can effectively determine the location of partial discharge sources in practical partial discharge measurement.

原文English
頁(從 - 到)613-627
頁數15
期刊Electric Power Components and Systems
40
發行號6
DOIs
出版狀態Published - 2012 3月 29

All Science Journal Classification (ASJC) codes

  • 能源工程與電力技術
  • 機械工業
  • 電氣與電子工程

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