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《Power System Technology》 2007-01
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A Power Network Fault Diagnosis Method Based on Rough Set Theory and Naive Bayesian Networks

ZHANG Yao-tian~1 HE Zheng-you~1 ZHAO Jing~1 ZHANG Peng~1 LIMing~3 GUI Jian-ting~3 1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan Province, China; 2.Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control(North China Electric Power University), Ministry of Education, Changping District, Beijing 102206, China; 3. Nanyang Power Supply Company of Henan Provincial Electric Power Company, Nanyang 473001, Henan Province, China  
When power network is in fault,if the fault information is imperfect and indeterminate or even the key information is lost,it may result in the condition that correct conclusion could not be given by fault diagnosis.To settle this problem the authors propose a new method to diagnose faults in power network in which the rough set theory is integrated with naive Bayesian networks.At first,the protections and circuit breakers are taken as conditional attributes and faulty region as decision-making attribute,various faults are investigated and decision table is established;then by use of attribute reducing method based on cognizable matrix and information entropy the optimal attribute reduction combination is extracted;finally,by means of the reduction decision table formed by optimal attribute reduction combination,the naive Bayesian networks model is built and the nodal probability is trained.The fault diagnosis software is programmed by VC++ programming language.Results of calculation examples show that the proposed method is correct and effective,and can improve the fault tolerance capability of the fault diagnosis system while the kernel attribute is lost,so this method is available.
【Fund】: 四川省杰出青年基金项目(06ZQ026-012)。
【CateGory Index】: TM711
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