A Power Network Fault Diagnosis Method Based on Rough Set Theory and Naive Bayesian Networks
Yaotian Zhang · Power System Technology · 2007
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.