摘要
由于电力变压器故障诊断中的测试数据信息不完备、有偏差,且贝叶斯网络处理不确定性问题能力强,文中提出了用于变压器故障诊断的NB、TAN和BAN三种贝叶斯分类器模型,并提出了贝叶斯网络分类器与粗糙集相结合的变压器故障诊断的新方法,它综合使用溶解气体分析结果和其它电气试验结果作为故障分类所需的属性。其相应的混合分类器为NB粗集、TAN粗集和BAN粗集分类器。实验表明提出的三种混合分类器都适于变压器故障诊断,具有处理信息缺失多的能力和容错特性,克服了粗糙集刚性推理的弱点,其性能明显优于单独使用贝叶斯网络分类器或粗糙集的方法。
As available testing data for transformer fauldiagnosis are incomplete and biased, and a Bayesian networkhas strong capability of processing uncertain information, NB(naive Bayesian) classifier model, TAN (tree augmented naiveBayesian) classifier model and BAN (Bayesian networkaugmented na?ve Bayesian) classifier model for transformersfault diagnosis are presented. To ensure the diagnosingcorrectness when there is shortage of several transformer testingdata, a new diagnosing approach, which integrates the Bayesiannetwork classifiers with rough set (RS), is proposed initially. Theapproach uses the results of dissolved gas-in-oil analysis (DGAand conventional electrical tests as the necessary attributes toclassify power transformer’s fault types. The relating hybridclassifiers are NB-RS, TAN-RS and BAN-RS, have strongability to deal with the lack of data, and have the error-tolerancecapability. So they have overcome the weakness of theover-rigidity of rough set based diagnosing approach. Thecomputing tests of diagnosing actual samples of transformefaults show that the diagnosing performance of the proposedhybrid approach prevails that of separated Bayesian networkbased classifiers and the rough set based approach.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2005年第10期159-165,共7页
Proceedings of the CSEE
关键词
电力变压器
故障诊断
贝叶斯分类器
粗糙集
Power transformer
Fault diagnosis
Bayesiannetwork classifier
Rough set
Uncertain inference
