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http://dx.doi.org/10.5370/JEET.2016.11.1.192

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)
Publication Information
Journal of Electrical Engineering and Technology / v.11, no.1, 2016 , pp. 192-199 More about this Journal
Abstract
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.
Keywords
Partial discharge; Characteristic quantity; Simulated annealing; Support vector machine; Pattern recognition;
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