基于粗糙集理论和朴素贝叶斯网络的电网故障诊断方法

电网发生故障后，当故障信息存在不完整或不确定性，甚至关键信息丢失时，会导致故障诊断难以得出正确结论。针对此问题，文章提出了一种粗糙集理论和朴素贝叶斯网络相结合的电网故障诊断方法。首先以保护、断路器作为条件属性，故障区域作为决策属性，考察各种故障情况并建立决策表，然后利用基于可辨识矩阵和信息熵的属性约简方法提取最佳属性约简组合，最后以最佳属性约简组合形成的约简决策表建立朴素贝叶斯网络模型，并对节点概率进行训练。运用VC++编写了基于该方法的故障诊断软件，算例结果表明，该方法正确、有效，能提高系统在丢失核属性情况下的容错性，具有较好的实用价值。

A Power Network Fault Diagnosis MethodBased on Rough Set Theory and Naive Bayesian Networks

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.