Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration. (2nd January 2013)

• Record Type: Journal Article
• Authors: Sadeghkhani, Iman; Ketabi, Abbas; Feuillet, Rene
• Other Names: Mitchell Richard Academic Editor.
• Is Part Of: Advances in artificial intelligence. Volume 2013(2013)
• Publication Date: 2013-01-02
• Subjects: Artificial intelligence -- Periodicals; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1155/2013/316985
• Languages: English
• ISSNs: 1687-7470

Abstract

Abstract : 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.