The fault diagnosis of power transformer using clustering and Radial Basis Function neural network

In paper, a fault diagnosis method of power transformer based on the radial basis function (RBF) neural network and clustering is discussed. It uses the clustering algorithm to decide centers of the radial basis function, and then uses least mean square (LMS) to calculate the output weights between...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 3; pp. 1257 - 1260
Main Author Li Chao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Artificial neural networks
Clustering algorithm
Clustering algorithms
Dissolved gas analysis
Fault diagnosis
Least mean square
Machine learning
Machine learning algorithms
Neural networks
Oil insulation
Power transformer
Power transformers
Radial basis function networks
RBF neural network
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Summary:In paper, a fault diagnosis method of power transformer based on the radial basis function (RBF) neural network and clustering is discussed. It uses the clustering algorithm to decide centers of the radial basis function, and then uses least mean square (LMS) to calculate the output weights between the hidden layer and output layer. After decided the architecture of the artificial neural network, uses the history data of power transformer to test the proposed diagnosis system. From the testing result, it can be concluded that the proposed method is efficient in transformer fault diagnosis.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212287