Radial Basis Function Neural Network Application to Power System Restoration Studies. (26th June 2012)

• Record Type: Journal Article
• Authors: Sadeghkhani, Iman; Ketabi, Abbas; Feuillet, Rene
• Other Names: Dauwels Justin Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2012(2012)
• Publication Date: 2012-06-26
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2012/654895
• Languages: English
• ISSNs: 1687-5265

Abstract

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.