Radial basis function neural network application to power system restoration studies

Authors:
Iman Sadeghkhani;Abbas Ketabi;Rene Feuillet
Affiliations:
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran;Department of Electrical Engineering, University of Kashan, Kashan, Iran;Grenoble Electrical Engineering Lab (G2ELab), Grenoble INP, Saint Martin d'Hères, Cedex, France
Venue:
Computational Intelligence and Neuroscience
Year:
2012

Citing 1
Cited 1

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation

Artificial-intelligence-based techniques to evaluate switching overvoltages during power system restoration

Advances in Artificial Intelligence

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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.