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Risk-based voltage collapse assessment using generalized regression neural network

This paper describes the implementation of a fast and accurate intelligent technique using generalized regression neural network to assess the risk of voltage collapse in power systems. The risk of voltage collapse is defined as the product of the probability of transmission line outage and its seve...

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Main Authors: Marsadek, M., Mohamed, A., Nopiah, Z.M.
Format: Article
Language:en_US
Published: 2017
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spelling my.uniten.dspace-58732018-01-17T04:10:40Z Risk-based voltage collapse assessment using generalized regression neural network Marsadek, M. Mohamed, A. Nopiah, Z.M. This paper describes the implementation of a fast and accurate intelligent technique using generalized regression neural network to assess the risk of voltage collapse in power systems. The risk of voltage collapse is defined as the product of the probability of transmission line outage and its severity associated with voltage collapse. The effect of weather in the probability of transmission line outage is taken into account in which the failure rate of each transmission line with respect to weather conditions is calculated. A new severity function model that utilises the voltage collapse prediction index is also considered in this assessment method. The performance of the generalised regression neural network is evaluated using mean absolute and mean square errors. The proposed risk based voltage collapse assessment method has been validated on a real power system. © 2011 IEEE. 2017-12-08T07:32:24Z 2017-12-08T07:32:24Z 2011 Article 10.1109/ICEEI.2011.6021767 en_US In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, ICEEI 2011 [6021767]
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 This paper describes the implementation of a fast and accurate intelligent technique using generalized regression neural network to assess the risk of voltage collapse in power systems. The risk of voltage collapse is defined as the product of the probability of transmission line outage and its severity associated with voltage collapse. The effect of weather in the probability of transmission line outage is taken into account in which the failure rate of each transmission line with respect to weather conditions is calculated. A new severity function model that utilises the voltage collapse prediction index is also considered in this assessment method. The performance of the generalised regression neural network is evaluated using mean absolute and mean square errors. The proposed risk based voltage collapse assessment method has been validated on a real power system. © 2011 IEEE.
format Article
author Marsadek, M.
Mohamed, A.
Nopiah, Z.M.
spellingShingle Marsadek, M.
Mohamed, A.
Nopiah, Z.M.
Risk-based voltage collapse assessment using generalized regression neural network
author_facet Marsadek, M.
Mohamed, A.
Nopiah, Z.M.
author_sort Marsadek, M.
title Risk-based voltage collapse assessment using generalized regression neural network
title_short Risk-based voltage collapse assessment using generalized regression neural network
title_full Risk-based voltage collapse assessment using generalized regression neural network
title_fullStr Risk-based voltage collapse assessment using generalized regression neural network
title_full_unstemmed Risk-based voltage collapse assessment using generalized regression neural network
title_sort risk-based voltage collapse assessment using generalized regression neural network
publishDate 2017
_version_ 1644493795311484928
score 13.211869

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