Optimal voltage restoration in electric power system using genetic algorithms

Bibliographic Details
Main Authors: Ulinuha, A., Islam, Syed, Masoum, Mohammad
Other Authors: Proceedings of POWERCON 2008 and the 2008 IEEE Power India Conference
Format: Conference Paper
Published: IEEE Power and Energy Society 2008
Online Access:http://hdl.handle.net/20.500.11937/11484
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author Ulinuha, A.
Islam, Syed
Masoum, Mohammad
author2 Proceedings of POWERCON 2008 and the 2008 IEEE Power India Conference
author_facet Proceedings of POWERCON 2008 and the 2008 IEEE Power India Conference
Ulinuha, A.
Islam, Syed
Masoum, Mohammad
author_sort Ulinuha, A.
building Curtin Institutional Repository
collection Online Access
first_indexed 2025-11-14T06:55:11Z
format Conference Paper
id curtin-20.500.11937-11484
institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T06:55:11Z
publishDate 2008
publisher IEEE Power and Energy Society
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-114842017-09-13T15:54:44Z Optimal voltage restoration in electric power system using genetic algorithms Ulinuha, A. Islam, Syed Masoum, Mohammad Proceedings of POWERCON 2008 and the 2008 IEEE Power India Conference 2008 Conference Paper http://hdl.handle.net/20.500.11937/11484 10.1109/ICPST.2008.4745295 IEEE Power and Energy Society fulltext
spellingShingle Ulinuha, A.
Islam, Syed
Masoum, Mohammad
Optimal voltage restoration in electric power system using genetic algorithms
title Optimal voltage restoration in electric power system using genetic algorithms
title_full Optimal voltage restoration in electric power system using genetic algorithms
title_fullStr Optimal voltage restoration in electric power system using genetic algorithms
title_full_unstemmed Optimal voltage restoration in electric power system using genetic algorithms
title_short Optimal voltage restoration in electric power system using genetic algorithms
title_sort optimal voltage restoration in electric power system using genetic algorithms
url http://hdl.handle.net/20.500.11937/11484