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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

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