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《Proceedings of the CSEE》 2015-12
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Power System Fault Diagnosis Based on Improved Dynamic Adaptive Fuzzy Petri Nets and Back Propagation Algorithm

XIE Min;WU Yaxiong;YAN Yuanyuan;ZHU Yanhan;School of Electric Power Engineering, South China University of Technology;  
In order to reduce error caused by artificial subjective factors in power system fault diagnosis process and improve fault tolerance and accuracy of fuzzy Petri net models, a power system fault diagnosis method based on improved dynamic adaptive fuzzy Petri nets and back propagation algorithm was proposed. First, the general Petri net models were built by introducing complementary arcs tuple in dynamic adaptive fuzzy Petri nets, which is able to dynamically adapt to updated fuzzy knowledge in expert systems. Second, the back propagation algorithm of neural network was used to train model parameters. Finally, the adaptability and the fault tolerance of the algorithm were analyzed. Simulation results on 8-bus testing system and Siping actual power system in Jilin province indicate that the proposed method can make full use of the parallel processing capabilities of Petri nets. Simple and clear in derivation, satisfying diagnosis results with incomplete information can be obtained by the proposed algorithm in this paper, also with good fault tolerance.
【Fund】: 国家重点基础研究发展计划项目(973项目)(2013CB 228205);; 国家自然科学基金青年基金项目(50907023)~~
【CateGory Index】: TM73
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