摘要
针对电网故障诊断中,警报信息存在的不确定性和不完整性,提出了一种粗糙集结合时间贝叶斯网络的故障诊断方法。本方法利用粗糙集理论的知识约简和处理不确定信息的能力,分层挖掘故障警报信息,对多个电网区域的决策表进行属性优选和诊断规则提取。再利用时间贝叶斯网络对各个区域并行推理,实现故障诊断。经过算例仿真,验证了该方法诊断快速准确、容错能力强、应用灵活。
To deal with the uncertainties and imperfections of alarm information after fault, this paper proposes a fault diagnosis method based on rough nets and temporal Bayesian networks for power networks. Using the ability of rough nets to reduce knowledge and process indeterminate information, the hierarchical mining of substation’s fault diagnosis knowledge is carried out. Then the fault decision table of divided power network areas is optimized, and diagnose rules are extracted. Then fault diagnosis is finished through parallel reasoning of Bayesian networks. The test proves that this method can diagnose the fault rapidly and accurately, and has strong fault tolerance and adaptability.
出处
《电测与仪表》
北大核心
2013年第11期65-68,78,共5页
Electrical Measurement & Instrumentation
关键词
电力系统
故障诊断
粗糙集
贝叶斯网络
power system
fault diagnosis
rough nets
Bayesian networks
