摘要
针对大电网复杂拓扑结构的电网故障情况,先对其进行分割,提出一种基于粗糙集与贝叶斯网相融合的分层递归型诊断网络模型。利用粗糙集理论的知识约简和处理不确定信息的能力,对电网故障诊断知识进行分层挖掘,实行属性优选,再运用贝叶斯网络完成故障区域及故障元件的识别。该方法对复杂故障采用多区域并行诊断。算例结果表明,该方法正确、有效,能提高系统丢失关键信息情况下的容错性,实时性也很高,具有很好的实用价值。
Accordingtothefaultconditionsofcomplicatedtopologyandlarge-scalepowernetworks, an efficient method is proposed to partition the large-scale power networks. A hierarchical recursive fault diagnosis model is proposed based on rough set and Bayesian network. Using the ability of knowledge reduction and processing indeterminate information of rough set theory, the hierarchical mining ofsubstation＇s fault diagnosis knowledge is carried out and optimal seeking of attributes is performed. Then Bayesian network is applied to identify fault areas and fault components. For complicated fault, this paper adopts multi-area parallel diagnosis. Results of calculation examples show that the proposed method is correct and effective, and can improve the fault tolerance capability and speed of the fault diagnosis system while the kernel attribute is lost, so this method is available.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2011年第7期20-25,31,共7页
Power System Protection and Control
关键词
故障诊断
复杂故障
电网分割
粗糙集
贝叶斯
并行诊断
fault diagnosis
complex faults
network partition
rough set. Bayesian networks
parallel diagnosis
