Radial Basis Function Neural Network Application to Power System Restoration Studies
Joint Authors
Ketabi, Abbas
Sadeghkhani, Iman
Feuillet, Rene
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-06-26
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Sadeghkhani, Iman& Ketabi, Abbas& Feuillet, Rene. 2012. Radial Basis Function Neural Network Application to Power System Restoration Studies. Computational Intelligence and Neuroscience،Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-488670
Modern Language Association (MLA)
Sadeghkhani, Iman…[et al.]. Radial Basis Function Neural Network Application to Power System Restoration Studies. Computational Intelligence and Neuroscience No. 2012 (2012), pp.1-10.
https://search.emarefa.net/detail/BIM-488670
American Medical Association (AMA)
Sadeghkhani, Iman& Ketabi, Abbas& Feuillet, Rene. Radial Basis Function Neural Network Application to Power System Restoration Studies. Computational Intelligence and Neuroscience. 2012. Vol. 2012, no. 2012, pp.1-10.
https://search.emarefa.net/detail/BIM-488670
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-488670