Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration
Joint Authors
Ketabi, Abbas
Feuillet, Rene
Sadeghkhani, Iman
Source
Advances in Artificial Intelligence
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-01-02
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Science
Abstract EN
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.
American Psychological Association (APA)
Sadeghkhani, Iman& Ketabi, Abbas& Feuillet, Rene. 2013. Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration. Advances in Artificial Intelligence،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-462956
Modern Language Association (MLA)
Sadeghkhani, Iman…[et al.]. Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration. Advances in Artificial Intelligence No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-462956
American Medical Association (AMA)
Sadeghkhani, Iman& Ketabi, Abbas& Feuillet, Rene. Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration. Advances in Artificial Intelligence. 2013. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-462956
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-462956