基于不同RS与NN组合的数据挖掘配电网故障诊断模型  

ASSESSMENT OF DATA MINING MODEL BASED ON THE DIFFERENT COMBINATION ROUGH SET WITH NEURAL NETWORK FOR FAULT SECTION DIAGNOSIS OF DISTRIBUTION NETWORKS

作　　者：孙雅明[1] 廖志伟[1] 

机构地区：[1]天津大学电气与自动化工程学院,天津市300072

出　　处：《电力系统自动化》2003年第6期31-35,共5页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目 (5 98770 16 )

摘　　要：在将基于 RS(粗糙集 )理论的数据挖掘用于配电网故障定位诊断模型研究的基础上 ,提出和构造了 4类不同的 RS与 NN(神经网络 )组合的故障诊断模型 ,给出了 RS与 NN在 4类模型中实现不同的互补性、关联关系、应用机理和原则及相应的局限性。通过对 5类模型的仿真测试结果比较 ,证明数据挖掘模型潜在知识发现的重要意义 ,并对 4类模型中 RS的应用机理和性能作出全面的评估。文中对The study based on rough set theory (RST) and data mining (DM) is used for fault section diagnosis (FSD) of distribution networks. The four kinds of diagnosis models on the basis of the different combination RS with neural network (NN) are proposed. The different complementarity and associated relation, the different application mechanism and principle, the model limitation are presented by implementing RS and NN in four models and the all-around assessment of the application mechanism and performance of RS is given. Furthermore, comparisons of simulation results among the five kinds of models prove the significance of DMs discovery in hidden knowledge. The conclusion and assessment of each model have the same significance for fault diagnosis system in other fields.

关 键 词：配电网 故障定位诊断 容错性能 数据挖掘 粗糙集理论 神经网络 

分 类 号：TM769[电气工程—电力系统及自动化]