DocumentCode :
1423174
Title :
A Self-Adaptive RBF Neural Network Classifier for Transformer Fault Analysis
Author :
Meng, Ke ; Dong, Zhao Yang ; Wang, Dian Hui ; Wong, Kit Po
Author_Institution :
Dept. of Electr. Eng., Hong Kong Polytech. Univ., Hong Kong, China
Volume :
25
Issue :
3
fYear :
2010
Firstpage :
1350
Lastpage :
1360
Abstract :
A new hybrid self-adaptive training approach-based radial basis function (RBF) neural network for power transformer fault diagnosis is presented in this paper. The proposed method is able to generate RBF neural network models based on fuzzy c-means (FCM) and quantum-inspired particle swarm optimization (QPSO), which can automatically configure network structure and obtain model parameters. With these methods, the number of neuron, centers and radii of hidden layer activated functions, as well as output connection weights can be automatically calculated. This learning method is proved to be effective by applying the RBF neural network in the classification of five benchmark testing data sets, and power transformer fault data set. The results clearly demonstrated the improved classification accuracy compared with other alternatives and showed that it can be used as a reliable tool for power transformer fault analysis.
Keywords :
fault diagnosis; fuzzy set theory; particle swarm optimisation; power engineering computing; power transformers; radial basis function networks; fuzzy c-means; hybrid self-adaptive training approach-based radial basis function; power transformer fault diagnosis; quantum-inspired particle swarm optimization; self-adaptive RBF neural network classifier; Computational methods; particle swarm optimization; power transformer fault diagnosis; radial basis function (RBF) neural network;
fLanguage :
English
Journal_Title :
Power Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8950
Type :
jour
DOI :
10.1109/TPWRS.2010.2040491
Filename :
5418850
Link To Document :
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