In this paper, controlled energization transformers is done using Artificial Intelligence (AI) technique. Radial Basis\r\nFunction Neural Network (RBFNN) is selected as AI tool. The most effective method for the limitation of the switching overvoltages\r\nis controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch.\r\nWe introduce a harmonic index that it�s minimum value is corresponding to the best case switching time. ANN training is performed\r\nbased on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the\r\neffectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated.
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