?is paper presents an approach to the study of switching overvoltages during power equipment energization. Switching action is\none of the most important issues in the power system restoration schemes. ?is action may lead to overvoltages which can damage\nsome equipment and delay power system restoration. In this work, switching overvoltages caused by power equipment energization\nare evaluated using arti??cial-neural-network- (ANN-) based approach. Both multilayer perceptron (MLP) trained with Levenberg-\nMarquardt (LM) algorithm and radial basis function (RBF) structure have been analyzed. In the cases of transformer and shunt\nreactor energization, the worst case of switching angle and remanent ??ux has been considered to reduce the number of required\nsimulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the\nnetwork are used as ANN inputs. Developed ANN is tested for a partial of 39-bus New England test system, and results show the\neffectiveness of the proposed method to evaluate switching overvoltages.
Loading....