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Advances in Artificial Intelligence
Volume 2013 (2013), Article ID 316985, 8 pages
Research Article

Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration

1Department of Electrical Engineering, Islamic Azad University, Najafabad Branch, Najafabad 85141-43131, Iran
2Department of Electrical Engineering, University of Kashan, Kashan 87317-51167, Iran
3Grenoble Electrical Engineering Lab (G2ELab), Grenoble INP, BP46, 38402 Saint Martin d’Hères Cedex, France

Received 19 May 2012; Revised 31 July 2012; Accepted 23 October 2012

Academic Editor: Richard Mitchell

Copyright © 2013 Iman Sadeghkhani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents an approach to the study of switching overvoltages during power equipment energization. Switching action is one of the most important issues in the power system restoration schemes. This action may lead to overvoltages which can damage some equipment and delay power system restoration. In this work, switching overvoltages caused by power equipment energization are evaluated using artificial-neural-network- (ANN-) based approach. Both multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) algorithm and radial basis function (RBF) structure have been analyzed. In the cases of transformer and shunt reactor energization, the worst case of switching angle and remanent flux has been considered to reduce the number of required simulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. Developed ANN is tested for a partial of 39-bus New England test system, and results show the effectiveness of the proposed method to evaluate switching overvoltages.