The quality of electric power has been a constant topic of study, mainly because inherent problems to it can lead to great economic losses, especially in industrial processes. Among the various factors that affect power quality, those related to transients originating from capacitor bank (CB) switching in the primary distribution systems must be highlighted. This paper ‎presents an Artificial Neural Network (ANN)-based approach to ‎estimate the transient overvoltages due to capacitor ‎energization. In proposed methodology, Levenberg-Marquardt ‎second order method is used to train the multilayer perceptron. ANN training is based on equivalent parameters of the network. Therefore, trained ANN is applicable to every studied system. The ‎developed ANN is trained with the extensive simulated results, and ‎tested for typical cases. Then the new algorithms are presented and demonstrated for a partial of 39-bus New England test system. The simulated results ‎show that the proposed technique can estimate the peak values of switching overvoltages with good accuracy.

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Artificial neural networks‎ capacitor banks switching switching overvoltages switching overvoltages