Application of Neural Network Model in Insulating Oil Fault Diagnosis

Author

Liu Yuequn ; Fu Huilin ; Zhou Yucai

Author_Institution

Changsha Electr. Power Vocational Technol. Coll., Changsha, China

Volume

1

fYear

2010

fDate

13-14 March 2010

Firstpage

647

Lastpage

651

Abstract

This paper introduced the practical value of artificial neural network in fault diagnosis field, respectively, applied the BP network, ELMAN network, RBF neural networks insulating oil fault diagnosis. Simulation results show that, in the transformer fault diagnosis ,the results of using RBF neural network diagnostic were significantly better than the results of traditional BP network and the results of ELMAN network, furthermore, the training time is short, and response fast.

Keywords

backpropagation; fault diagnosis; radial basis function networks; transformer oil; transformers; BP network; ELMAN network; RBF neural networks; artificial neural network; insulating oil fault diagnosis; transformer fault diagnosis; Artificial neural networks; Data engineering; Data processing; Design engineering; Fault diagnosis; Neural networks; Oil insulation; Petroleum; Power engineering and energy; Power transformer insulation; BP network; ELMAN network; Neural networks; RBF neural network; fault diagnosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on

Conference_Location

Changsha City

Print_ISBN

978-1-4244-5001-5

Electronic_ISBN

978-1-4244-5739-7

Type

conf

DOI

10.1109/ICMTMA.2010.727

Filename

5458825