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Delta-Bar-Delta and directed random search algorithms to study capacitor banks switching overvoltages

Author(s): Sadeghkhani Iman | Ketabi Abbas | Feuillet Rene

Journal: Serbian Journal of Electrical Engineering
ISSN 1451-4869

Volume: 9;
Issue: 2;
Start page: 217;
Date: 2012;
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Keywords: Artificial neural networks | Capacitor banks switching | Delta-bardelta | Directed random search | Switching overvoltages

This paper introduces an approach to analyse transient overvoltages during capacitor banks switching based on artificial neural networks (ANN). Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS) were used to train the ANNs. The ANN training is based on equivalent parameters of the network and therefore, a trained ANN is applicable to every studied system. The developed ANN is trained with extensive simulated results and tested for typical cases. The new algorithms are presented and demonstrated for a partial 39-bus New England test system. The simulated results show the proposed technique can accurately estimate the peak values of switching overvoltages.
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