Evolved intelligent clustered bee colony for voltage stability prediction on power transmission system

Very often, voltage instability causes millions of money to cater with the negative effects it gave to the people; therefore, it is very important that prior to this worst situation, some manual or automatic recovery system had to be turn on to minimize or totally avoid the entire situation. But the...

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Main Authors: Lim, Z. J., Mustafa, M. W.
Format: Article
Published: Springer Verlag 2016
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Online Access:http://eprints.utm.my/id/eprint/72217/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929096309&doi=10.1007%2fs00500-015-1697-2&partnerID=40&md5=a417c02713d07da23e2e72093ace3047
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spelling my.utm.722172017-11-22T12:07:36Z http://eprints.utm.my/id/eprint/72217/ Evolved intelligent clustered bee colony for voltage stability prediction on power transmission system Lim, Z. J. Mustafa, M. W. TK Electrical engineering. Electronics Nuclear engineering Very often, voltage instability causes millions of money to cater with the negative effects it gave to the people; therefore, it is very important that prior to this worst situation, some manual or automatic recovery system had to be turn on to minimize or totally avoid the entire situation. But these recovery systems will not be turn on if there is no good indication or alarm that controls or informs. In this paper, evolved intelligent clustered artificial bee colony (EICBC) is introduced to predict the voltage stability condition of the IEEE 30-bus test system. Fast Voltage Stability Index is utilized as an indicator to measure the distance of the power system network to the voltage collapse point when the reactive load is varied slowly as reactive load gives the most impact on the stability of the system. EICBC is able to converge faster to its best solution while maintaining the stability of the prediction system by avoiding local minima convergence. The results also show that the proposed algorithm is superior in the prediction accuracy and can be used to categorize the conditions of the network for the ease of identification. Springer Verlag 2016 Article PeerReviewed Lim, Z. J. and Mustafa, M. W. (2016) Evolved intelligent clustered bee colony for voltage stability prediction on power transmission system. Soft Computing, 20 (8). pp. 3215-3230. ISSN 1432-7643 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929096309&doi=10.1007%2fs00500-015-1697-2&partnerID=40&md5=a417c02713d07da23e2e72093ace3047
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Lim, Z. J.
Mustafa, M. W.
Evolved intelligent clustered bee colony for voltage stability prediction on power transmission system
description Very often, voltage instability causes millions of money to cater with the negative effects it gave to the people; therefore, it is very important that prior to this worst situation, some manual or automatic recovery system had to be turn on to minimize or totally avoid the entire situation. But these recovery systems will not be turn on if there is no good indication or alarm that controls or informs. In this paper, evolved intelligent clustered artificial bee colony (EICBC) is introduced to predict the voltage stability condition of the IEEE 30-bus test system. Fast Voltage Stability Index is utilized as an indicator to measure the distance of the power system network to the voltage collapse point when the reactive load is varied slowly as reactive load gives the most impact on the stability of the system. EICBC is able to converge faster to its best solution while maintaining the stability of the prediction system by avoiding local minima convergence. The results also show that the proposed algorithm is superior in the prediction accuracy and can be used to categorize the conditions of the network for the ease of identification.
format Article
author Lim, Z. J.
Mustafa, M. W.
author_facet Lim, Z. J.
Mustafa, M. W.
author_sort Lim, Z. J.
title Evolved intelligent clustered bee colony for voltage stability prediction on power transmission system
title_short Evolved intelligent clustered bee colony for voltage stability prediction on power transmission system
title_full Evolved intelligent clustered bee colony for voltage stability prediction on power transmission system
title_fullStr Evolved intelligent clustered bee colony for voltage stability prediction on power transmission system
title_full_unstemmed Evolved intelligent clustered bee colony for voltage stability prediction on power transmission system
title_sort evolved intelligent clustered bee colony for voltage stability prediction on power transmission system
publisher Springer Verlag
publishDate 2016
url http://eprints.utm.my/id/eprint/72217/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929096309&doi=10.1007%2fs00500-015-1697-2&partnerID=40&md5=a417c02713d07da23e2e72093ace3047
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score 13.214268