Risk based security assessment of power system using generalized regression neural network with feature extraction

A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle compo...

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Main Authors: Marsadek, M., Mohamed, A.
Format: Article
Language:en_US
Published: 2017
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spelling my.uniten.dspace-58722018-01-17T04:05:50Z Risk based security assessment of power system using generalized regression neural network with feature extraction Marsadek, M. Mohamed, A. A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy. © 2013 Central South University Press and Springer-Verlag Berlin Heidelberg. 2017-12-08T07:32:24Z 2017-12-08T07:32:24Z 2013 Article 10.1007/s11771-013-1508-9 en_US Risk based security assessment of power system using generalized regression neural network with feature extraction. Journal of Central South University, 20(2), 466-479.
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language en_US
description A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy. © 2013 Central South University Press and Springer-Verlag Berlin Heidelberg.
format Article
author Marsadek, M.
Mohamed, A.
spellingShingle Marsadek, M.
Mohamed, A.
Risk based security assessment of power system using generalized regression neural network with feature extraction
author_facet Marsadek, M.
Mohamed, A.
author_sort Marsadek, M.
title Risk based security assessment of power system using generalized regression neural network with feature extraction
title_short Risk based security assessment of power system using generalized regression neural network with feature extraction
title_full Risk based security assessment of power system using generalized regression neural network with feature extraction
title_fullStr Risk based security assessment of power system using generalized regression neural network with feature extraction
title_full_unstemmed Risk based security assessment of power system using generalized regression neural network with feature extraction
title_sort risk based security assessment of power system using generalized regression neural network with feature extraction
publishDate 2017
_version_ 1644493795022077952
score 13.214268