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

Author

Huilan, Jiang ; Ying, Guan ; Dongwei, Li ; Jianqiang, Xu

Author_Institution

Sch. of Electr. Eng. & Autom., Tianjin Univ.

fYear

2005

fDate

2005

Firstpage

1

Lastpage

6

Abstract

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

Keywords

fault diagnosis; fault tolerance; power system analysis computing; power system faults; power transmission lines; radial basis function networks; RBF neural network; fault diagnosis; fault tolerance; high voltage transmission lines; information processing system; power system faults; radial basis function; self-adaptive clustering algorithm; Clustering algorithms; Fault diagnosis; Fault tolerance; Neural networks; Pattern recognition; Power system faults; Power system simulation; Power transmission lines; Radial basis function networks; Voltage; RBFNN; fault diagnosis; self adaptive clustering algorithm; transmission line;

fLanguage

English

Publisher

ieee

Conference_Titel

Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES

Conference_Location

Dalian

Print_ISBN

0-7803-9114-4

Type

conf

DOI

10.1109/TDC.2005.1547050

Filename

1547050