The application of Bayesian network theory in transformer condition assessment

Author

Jiangtao Quan ; Ling Ruan ; Zhicheng Xie ; Xingdong Li ; Xiangning Lin

Author_Institution

State Grid Key Lab. of On-site Test Technol. on High Voltage Power Apparatus, Hubei Electr. Power Test & Res. Inst., Wuhan, China

fYear

2013

fDate

8-11 Dec. 2013

Firstpage

1

Lastpage

4

Abstract

In order to assess transformer´s status and forecast its potential fault, this paper applied Bayesian network classifier into transformer fault diagnosis, combined with dissolved gas analysis and other electrical test results, and thereby created a transformer fault synthetic diagnosis method. Build up transformer´s fault diagnosis model based on Naive Bayesian Classifier and Tree Augmented Naive Bayesian Classifier respectively, and verify their validity by instance.

Keywords

Bayes methods; chemical analysis; fault diagnosis; transformer testing; Bayesian network classifier; build up transformer; dissolved gas analysis; electrical test results; transformer fault synthetic diagnosis method; transformer status; tree augmented naive Bayesian classifier; Bayes methods; Circuit faults; Fault diagnosis; Grounding; Oil insulation; Power transformer insulation; Bayesian Network; Fault diagnosis; NBC; TAN; Transformer´s status;

fLanguage

English

Publisher

ieee

Conference_Titel

Power and Energy Engineering Conference (APPEEC), 2013 IEEE PES Asia-Pacific

Conference_Location

Kowloon

Type

conf

DOI

10.1109/APPEEC.2013.6837161

Filename

6837161