Research of Power Transformer Fault Diagnosis System Based on Rough Sets and Bayesian Networks

Qin Li; Zhi Bin Li; Qi Zhang

doi:10.4028/www.scientific.net/AMR.320.524

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 320Research of Power Transformer Fault Diagnosis...

Research of Power Transformer Fault Diagnosis System Based on Rough Sets and Bayesian Networks

Abstract:

As one of the most important electric equipment for reliable power supply, the secure operation of power transformer must be guaranteed. Three-ratio method based on the Dissolved Gases Analysis (DGA) is most widely used for transformer fault diagnosis currently. Its advantage is simple and easy to use, but its encoding is incomplete and the faults classification zone is over absolute. This paper combines rough sets and Bayesian Network. Rough sets is used to get useful characters, simplify data sets, obtain simplification rules and the minimum property sets; Bayesian Network is used to analyze the faults caused by uncertain elements in complex system. The fault diagnostic model is built by Bayesian Network Tool (BNT) in MATLAB, and the simulation result shows the validity of this method.

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Periodical:

Advanced Materials Research (Volume 320)

Pages:

524-529

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.320.524

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Online since:

August 2011

Authors:

Qin Li, Zhi Bin Li, Qi Zhang

Keywords:

Bayesian Network (BN), Fault Diagnosis, MATLAB BNT, Rough Set (RS), Transformer

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