基于主成分分析法和贝叶斯网络的智能变电站故障诊断方法
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摘要
提出将主成分分析法应用于贝叶斯网络,通过对故障源的简化与故障特征的压缩,得到最简故障特征表,降低操作系统的复杂性。根据变电站运行模式和保护措施建立相应的贝叶斯网络模型,利用贝叶斯网络的变量消元法对220k V变电站进行实例分析并对其故障概率进行推理。实验结果证明:该方法利于描述故障特征的变化,对变电站故障原因的分析直观可靠。
Applying the principal component analysis( PCA) to Bayesian networks to simplify the failure sources and compress the fault features and to obtain the minimum diagnosis scale and reduce the complexity of the operating system was proposed. Through considering the substation's operation mode and protective measures,a corresponding Bayesian network model was established and the Bayesian network's variable elimination method was adopted to analyze a 220 k V substation and ratiocinate its fault probability. The experimental results show that,this method can describe the change of fault characteristics and the analysis of the substation fault causes is direct and reliable.
Applying the principal component analysis( PCA) to Bayesian networks to simplify the failure sources and compress the fault features and to obtain the minimum diagnosis scale and reduce the complexity of the operating system was proposed. Through considering the substation's operation mode and protective measures,a corresponding Bayesian network model was established and the Bayesian network's variable elimination method was adopted to analyze a 220 k V substation and ratiocinate its fault probability. The experimental results show that,this method can describe the change of fault characteristics and the analysis of the substation fault causes is direct and reliable.
引文
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[6]韩璞,张德利,韩晓娟,等.基于主成分分析法与贝叶斯网络的汽轮机故障诊断方法[J].热能动力工程,2008,23(3):244~247.
[2]马智明,阳宪惠.主成分分析法在故障诊断中的应用[J].数理统计与管理,2000,19(6):39~40.
[3]张德利,韩璞,张楠.贝叶斯网络推理简化算法的研究[C].第五届全国信息获取与处理学术会议.沈阳:中国仪器仪表学会,2007:580~582.
[4]Carbonetto P,Stephens Y M.Scalable Variational Inference for Bayesian Variable Selection in Regression,and Its Accuracy in Genetic Association Studies[J].Bayesian Analysis,2012,7(1):73~107.
[5]华斌,周建中,喻菁.贝叶斯网络在水电机组故障诊断中的应用研究[J].华北电力大学学报,2004,31(5):33~36.
[6]韩璞,张德利,韩晓娟,等.基于主成分分析法与贝叶斯网络的汽轮机故障诊断方法[J].热能动力工程,2008,23(3):244~247.