NDLI: The application of Bayesian network theory in transformer condition assessment

Content Provider	IEEE Xplore Digital Library
Author	Jiangtao Quan Ling Ruan Zhicheng Xie Xingdong Li Xiangning Lin
Copyright Year	2013
Description	Author affiliation: State Key Lab. of Adv. Electromagn. Eng. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China (Zhicheng Xie; Xingdong Li; Xiangning Lin) \|\| State Grid Key Lab. of On-site Test Technol. on High Voltage Power Apparatus, Hubei Electr. Power Test & Res. Inst., Wuhan, China (Jiangtao Quan; Ling Ruan)
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.
Sponsorship	IEEE Hong Kong Sect., IE/PE/IA/PEL Joint Chapt.
Starting Page	1
Ending Page	4
File Size	746590
Page Count	4
File Format	PDF
ISBN	9781479925223
DOI	10.1109/APPEEC.2013.6837161
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis TAN Transformer's status Grounding Bayesian Network Oil insulation Bayes methods Circuit faults NBC Power transformer insulation
Content Type	Text
Resource Type	Article

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