Classification of partial discharge events in GILBS using discrete wavelet transform and probabilistic neural networks

Ming Shou Su, Jiann-Fuh Chen, Chien Yi Chen, Cheng-Chi Tai, Yu Hsun Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes an approach to determining classification of partial discharge (PD) events in Gas Insulated Load Break Switches (GILBS). Discrete wavelet transform (DWT) is employed to suppress noises of measured signals by the high-frequency current transformer (HFCT). Three kinds of different defects are designed and placed inside three GILBS individually. For accurately determination of the different defect, feature extraction and statistics analysis of the measured signals are used in the proposed method. Finally, experimental results validate that the proposed approach can effectively discriminate the PD events in GILBS.

Original languageEnglish
Title of host publicationProceedings of 2012 IEEE International Conference on Condition Monitoring and Diagnosis, CMD 2012
Pages963-966
Number of pages4
DOIs
Publication statusPublished - 2012 Dec 1
Event2012 IEEE International Conference on Condition Monitoring and Diagnosis, CMD 2012 - Bali, Indonesia
Duration: 2012 Sep 232012 Sep 27

Publication series

NameProceedings of 2012 IEEE International Conference on Condition Monitoring and Diagnosis, CMD 2012

Other

Other2012 IEEE International Conference on Condition Monitoring and Diagnosis, CMD 2012
CountryIndonesia
CityBali
Period12-09-2312-09-27

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality

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  • Cite this

    Su, M. S., Chen, J-F., Chen, C. Y., Tai, C-C., & Lin, Y. H. (2012). Classification of partial discharge events in GILBS using discrete wavelet transform and probabilistic neural networks. In Proceedings of 2012 IEEE International Conference on Condition Monitoring and Diagnosis, CMD 2012 (pp. 963-966). [6416314] (Proceedings of 2012 IEEE International Conference on Condition Monitoring and Diagnosis, CMD 2012). https://doi.org/10.1109/CMD.2012.6416314