Classification of partial discharge events in GILBS using probabilistic neural networks and the fuzzy c-means clustering approach

Ming Shou Su, Chung Chu Chia, Chien Yi Chen, Jiann Fuh Chen

研究成果: Article同行評審

24 引文 斯高帕斯(Scopus)

摘要

In this paper, we present an approach to determining classification of partial discharge (PD) events in Gas Insulated Load Break Switches (GILBS). Probabilistic neural networks (PNN) and the fuzzy C-means (FCM) clustering approach are used as the classification method. Discrete wavelet transform (DWT) is employed to suppress noises in the measured signals of high-frequency current transformers (HFCT). SF6 is an insulation gas in Gas Insulated Switchgear (GIS) and GILBS. Three kinds of different defects are designed and placed inside three GILBS. The proposed method greatly improves the classification correctness ratios of the defect models using conventional observations of phase resolved partial discharge (PRPD). To accurately determine the different defect models, the proposed method uses feature extraction and statistical analysis of the measured signals. Finally, experimental results validate that the proposed approach can effectively classify the PD events in GILBS.

原文English
頁(從 - 到)173-179
頁數7
期刊International Journal of Electrical Power and Energy Systems
61
DOIs
出版狀態Published - 2014 10月

All Science Journal Classification (ASJC) codes

  • 能源工程與電力技術
  • 電氣與電子工程

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