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Genetic algorithm based neural networks applied to fault classification for EHV transmission lines with a UPFC

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4 Author(s)
Song, Y.H. ; Brunel Univ., Uxbridge, UK ; Johns, A.T. ; Xuan, Q.Y. ; Liu, J.Y.

The paper proposes a novel fault detection and classification scheme for EHV power transmission lines using genetic algorithm-based neural networks. The application concerned is fault classification for EHV lines with a unified power factor corrector (UPFC), since fault classification is a key part of protective relaying schemes. After the genetic algorithm-based neural network is briefly discussed in general, EMTP based digital simulation results of a UPFC transmission system are presented. The generation of training/test data and preprocessing of these data for neural networks are then described. The paper places special emphasis on the performance comparison between a genetic algorithm-based neural network and a backpropagation network-based scheme

Published in:

Developments in Power System Protection, Sixth International Conference on (Conf. Publ. No. 434)

Date of Conference:

25-27 Mar 1997

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