DocumentCode
3573746
Title
The Fault Diagnosis of Power Transformer Based on Improved RBF Neural Network
Author
Guo, Ying-Jun ; Li-Hua Sun ; Liang, Yong-Chun ; Ran, Hai-chao ; Hui-Qin Sun
Author_Institution
Hebei Univ. of Sci. & Technol., Shijiazhuang
Volume
2
fYear
2007
Firstpage
1111
Lastpage
1114
Abstract
The radial basis function (RBF) neural network is prior to BP neural network in the ability of approach, the ability of classification and the rate of train. A fault diagnosis method of power based on the RBF neural network is discussed in this paper. The example shows that two input vectors of different class may be more near than two input vectors of the same class. In order to overcome this defect, improve the ability of approach and the ability of classification, the input data is processed according to data reliability analysis and the center of RBF is trained according to the class of input data. The effect of improvement of RBF network has been approved in the fault diagnosis of power transformer.
Keywords
backpropagation; fault diagnosis; power engineering computing; power transformers; radial basis function networks; BP neural network; RBF neural network; data reliability analysis; fault diagnosis; power transformer; radial basis function neural network; Cybernetics; Data analysis; Fault diagnosis; Frequency; Gaussian processes; Machine learning; Neural networks; Power system reliability; Power transformers; Vectors; Data reliability analysis; Fault diagnosis; Power transformer; RBF neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2007 International Conference on
Print_ISBN
978-1-4244-0973-0
Electronic_ISBN
978-1-4244-0973-0
Type
conf
DOI
10.1109/ICMLC.2007.4370310
Filename
4370310
Link To Document