Power Transformer Fault Diagnosis Based on Fuzzy Integral Fusion

Author

Ling, Zhou ; Huimin, Yan ; Yonggang, Cao

Author_Institution

Hohai Univ.

Volume

3

fYear

2006

fDate

6-8 Sept. 2006

Firstpage

1087

Lastpage

1090

Abstract

A new method to identify power transformer faults based on fuzzy integral fusion is presented. Firstly, the membership function of the value of three-ratio are presented and the data of the value of three ratios are processed by fuzzy method. Then the faults are identified by four different radial basis function (RBF) neural networks. Finally results of identification from four different neural networks are fused by fuzzy integral fusion and then form the final diagnosis. The results of simulation by presented method have been proved to be more accuracy than single neural network in identifying the practical transformer fault case

Keywords

fault diagnosis; fuzzy set theory; power engineering computing; power transformers; radial basis function networks; RBF neural network; fault diagnosis; fuzzy integral fusion; membership function; power transformer; radial basis function; Agriculture; Dissolved gas analysis; Electrical equipment industry; Equations; Fault diagnosis; Fuzzy neural networks; Fuzzy systems; Hydrogen; Neural networks; Power transformers; fault diagnosis; fuzzy integral; power transformer; radial basis function (RBF);

fLanguage

English

Publisher

ieee

Conference_Titel

Universities Power Engineering Conference, 2006. UPEC '06. Proceedings of the 41st International

Conference_Location

Newcastle-upon-Tyne

Print_ISBN

978-186135-342-9

Type

conf

DOI

10.1109/UPEC.2006.367645

Filename

4218853