Journal of Electrical Engineering and Technology

  • 제11권1호
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  • Pages.192-199
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  • 2016
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  • 1975-0102(pISSN)
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  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

  • Tang, Ju (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
  • Zhuo, Ran (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
  • Wang, DiBo (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
  • Wu, JianRong (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University) ;
  • Zhang, XiaoXing (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University)
  • 투고 : 2013.11.06
  • 심사 : 2015.05.26
  • 발행 : 2016.01.01
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초록

With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. The UHF signal and pulse current signal of four kinds of typical artificial defect models in gas insulated switchgear (GIS) are obtained simultaneously by experiment. The relationship map of ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge of four kinds of typical artificial defect models are plotted. UHF cumulative energy and its corresponding apparent discharge are used as inputs. The support vector machine (SVM) incremental method is constructed. Examples show that the PD SVM incremental method based on simulated annealing (SA) effectively speeds up the data update rate and improves the adaptability of the classifier compared with the original method, in that the total sample is constituted by the old and new data. The PD SVM incremental method is a better pattern recognition technology for PD on-line monitoring.

키워드

참고문헌

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피인용 문헌

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  2. Failure Prognosis of High Voltage Circuit Breakers with Temporal Latent Dirichlet Allocation vol.10, pp.12, 2017, https://doi.org/10.3390/en10111913
  3. Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance vol.10, pp.10, 2017, https://doi.org/10.3390/en10101516
  4. Feature extraction of GIS partial discharge signal based on S-transform and singular value decomposition vol.11, pp.2, 2017, https://doi.org/10.1049/iet-smt.2016.0255