Journal
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
Volume 61, Issue -, Pages 173-179Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2014.03.054
Keywords
Partial discharge; Gas Insulated Load Break Switches; Discrete wavelet transform; Probabilistic neural networks; Fuzzy c-means clustering approach
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Funding
- National Science Council of the ROC [NSC 102-2221-E-006-109]
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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. (C) 2014 Elsevier Ltd. All rights reserved.
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