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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/397

Title: An accurate fault classification algorithm using a minimal radial basis function neural network
Authors: Dash, P K
Samantaray, S R
Keywords: RBF neural network
Fault Classification
Network Input Generation
Issue Date: 2004
Publisher: CRC Publications
Citation: Engineering Intelligent Systems, Vol 4, P 205-210
Abstract: The paper presents a new fault classification scheme for high speed relaying using minimal radial basis function neural network. Unlike earlier approaches in using radial basis function network, the new approach reduces the training time drastically and provides a systematic framework for selecting the number of neurons in the hidden layer. Further the minimal radial basis function network yields an accurate fault type classification on a transmission line even in the presence of high fault resistance in the fault path. The paper also presents two different approaches in generating the inputs to the neural network with a view to simplify the training procedure and reduce the complexity in calculations. Several computer simulated test results are presented to highlight the effectiveness of the new approach.
Description: Copyright for this article belongs to the publisher
URI: http://hdl.handle.net/2080/397
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