Self-adaptive Clustering Algorithm Based RBF Neural Network and its Application in the Fault Diagnosis of Power Systems

Radial basis function (RBF) neural networks (NNs) have been used in pattern recognition. The application of RBF network for fault diagnosis in high voltage transmission lines is presented in this paper. A self-adaptive clustering algorithm is proposed for the clustering process of RBFNN. The results...

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Veröffentlicht in:2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific S. 1 - 6
Hauptverfasser: Jiang Huilan, Guan Ying, Li Dongwei, Xu Jianqiang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 2005
Schlagworte:
Clustering algorithms
Fault diagnosis
Fault tolerance
Neural networks
Pattern recognition
Power system faults
Power system simulation
Power transmission lines
Radial basis function networks
RBFNN
self adaptive clustering algorithm
transmission line
Voltage
ISBN:0780391144, 9780780391147
ISSN:2160-8636
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Zusammenfassung:Radial basis function (RBF) neural networks (NNs) have been used in pattern recognition. The application of RBF network for fault diagnosis in high voltage transmission lines is presented in this paper. A self-adaptive clustering algorithm is proposed for the clustering process of RBFNN. The results of the simulation and fault tolerance test confirm that the proposed method can diagnose the fault of high voltage transmission lines quickly and correctly. Furthermore, it has the fault-tolerant ability that can identify the distorted input signals caused by the disturbance, and therefore it has the practical application value for real-timing information processing system
ISBN:0780391144
9780780391147
ISSN:2160-8636
DOI:10.1109/TDC.2005.1547050