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Study on Bayesian Network Parameters Learning of Power System Component Fault Diagnosis Based on Particle Swarm Optimization

Author(s): Qingxi Shia, Sujie Lianga, Wei Feia, Yongfeng Shib, Ruifeng Shib*
a Linyi Power Supply Company, Shandong Electric Power Corporation, No. 130, Jinque RD, Linyi 276000, China
b School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
International Journal of Smart Grid and Clean Energy, vol. 2, no. 1, January 2013: pp. 132–137
ISSN: 2315-4462
Digital Object Identifier: 10.12720/sgce.2.1.132-137

Abstract: Power system component fault diagnosis problem is a key issue in case of the failure of the power system. A Bayesian network, in which the network parameters are learnt by a particle swarm optimization algorithm, is proposed in this paper to establish the statistical diagnosis model. The Noisy-Or and Noisy-And structure are employed to construct the framework of the model, where the 4-level Bayesian network makes the fault prediction with properly given parameters. In order to verify the performance of our proposed method, a typical power system component fault diagnosis problem is used for empirical case study, and the result demonstrates the effectiveness of the proposed method.
Keywords: Power system; component fault diagnosis; Bayesian networks; parameters learning; particle swarm optimization

Full Paper.pdf

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