Title :
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
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;
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
DOI :
10.1109/ICMTMA.2010.727
