Research of bayesian networks application to transformer fault diagnosis

Authors:
Qin Li;Zhibin Li;Qi Zhang;Liusu Zeng
Affiliations:
School of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China and CIMS Research Centre, Tongji University, Shanghai, China;School of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China;School of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China;School of Electric Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai, China
Venue:
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Year:
2011

Citing 1
Cited 0

Bayesian Network Classifiers

Machine Learning - Special issue on learning with probabilistic representations

Quantified Score

Hi-index	0.00

Visualization

Abstract

The power transformer as the key equipment in electrical power systems, its operation reliability directly influences security of electrical power systems. Three-ratio method based on the Dissolved Gases Analysis is most widely used for transformer fault diagnosis currently. Considering the incomplete encoding and the over absolute faults classification zone of threeratio method, this paper proposes no-code ratio method and Bayesian Network to diagnose the faults of transformer. The Bayesian Network diagnostic model is built by Bayesian Network Tool in MATLAB, and the simulation result shows the validity of this method.