Rough set and radial basis function neural network based insulation data mining fault diagnosis for power transformer

作     者：董立新 肖登明 刘奕路 

作者机构：Institute of Electronic Information & Electrical Engineering Shanghai Jiaotong University Dept. of Electrical and Computer Engineering Virginia Tech Blacksburg VA 24061 USA 

出 版 物：《Journal of Harbin Institute of Technology(New Series)》 (哈尔滨工业大学学报（英文版）)

年 卷 期：2007年第14卷第2期

页      面：263-268页

核心收录：

学科分类：0810[工学-信息与通信工程] 080801[工学-电机与电器] 0808[工学-电气工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the National Natural Science Foundation of China (Grant No. 50128706) 

主　　题：rough set (RS) radial basis function neural network (RBFNN) data mining fault diagnosis 

摘      要：Rough set (RS) and radial basis function neural network (RBFNN) based insulation data mining fault diagnosis for power transformer is proposed. On the one hand rough set is used as front of RBFNN to simplify the input of RBFNN and mine the rules. The mined rules whose “confidence and “support is higher than requirement are used to offer fault diagnosis service for power transformer directly. On the other hand the mining samples corresponding to the mined rule, whose “confidence and support is lower than requirement, are used to be training samples set of RBFNN and these samples are clustered by rough set. The center of each clustering set is used to be center of radial basis function, i.e., as the hidden layer neuron. The RBFNN is structured with above base, which is used to diagnose the case that can not be diagnosed by mined simplified valuable rules based on rough set. The advantages and effectiveness of this method are verified by testing.