SA-SVM incremental algorithm for GIS PD pattern recognition

Author

Di-bo Wang ; Ju Tang ; Ran Zhuo ; Jun-yi Lin ; Jian-rong Wu ; Xiao-Xing Zhang

Author_Institution

State Key Lab. of Power Transm. Equip. & Syst. Security & New Technol., Chongqing Univ., Chongqing, China

fYear

2012

fDate

17-20 Sept. 2012

Firstpage

388

Lastpage

391

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. Using ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge as inputs, a support vector machine (SVM) incremental method based on simulated annealing (SA) is constructed. Examples show that the new method speeds up the data update rate and improves the adaptability of the classifier.

Keywords

gas insulated switchgear; learning (artificial intelligence); partial discharges; pattern classification; power engineering computing; simulated annealing; support vector machines; GIS; PD; SA; SVM incremental method; UHF cumulative energy; insulated defect; online recognition rate; partial discharge; pattern classifier; pattern recognition; simulated annealing; support vector machine; ultra high frequency; Discharges (electric); Gas insulation; Optimization; Partial discharges; Pattern recognition; Support vector machines; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

High Voltage Engineering and Application (ICHVE), 2012 International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4673-4747-1

Type

conf

DOI

10.1109/ICHVE.2012.6357131

Filename

6357131