By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Jiang Huilan ; Sch. of Electr. Eng. & Autom., Tianjin Univ. ; Guan Ying ; Li Dongwei ; Xu Jianqiang

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

Published in:

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

Date of Conference: