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Computational Intelligence and Neuroscience
Volume 2012, Article ID 654895, 10 pages
http://dx.doi.org/10.1155/2012/654895
Research Article

Radial Basis Function Neural Network Application to Power System Restoration Studies

1Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
2Department of Electrical Engineering, University of Kashan, Kashan 8731751167, Iran
3Grenoble Electrical Engineering Lab (G2ELab), Grenoble INP, BP46, 38402 Saint Martin d'Hères, Cedex, France

Received 5 March 2012; Revised 29 April 2012; Accepted 1 May 2012

Academic Editor: Justin Dauwels

Copyright © 2012 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.

Abstract

One of the most important issues in power system restoration is overvoltages caused by transformer switching. These overvoltages might damage some equipment and delay power system restoration. This paper presents a radial basis function neural network (RBFNN) to study transformer switching overvoltages. To achieve good generalization capability for developed RBFNN, equivalent parameters of the network are added to RBFNN inputs. The developed RBFNN is trained with the worst-case scenario of switching angle and remanent flux and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can estimate the peak values and duration of switching overvoltages with good accuracy.