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dc.contributor.authorChan, Kit Yan
dc.contributor.authorYu, Changjun
dc.contributor.authorTeo, Kok Lay
dc.contributor.authorNordholm, Sven
dc.date.accessioned2017-01-30T12:38:40Z
dc.date.available2017-01-30T12:38:40Z
dc.date.created2015-02-02T20:00:45Z
dc.date.issued2014
dc.identifier.citationChan, K.Y. and Yu, C. and Teo, K.L. and Nordholm, S. 2014. Essential issues on solving optimal power flow problems using soft-computing. Numerical Algebra, Control and Optimization. 4 (4): pp. 341-351.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/23686
dc.identifier.doi10.3934/naco.2014.4.341
dc.description.abstract

Optimal power ow (OPF) problems are important optimization problems in power systems which aim to minimize the operation cost of generators so that the load demand can be met and the loadings are within the feasible operating regions of the generators. This brief paper emphasizes two essential issues related to solving the OPF problems and which are rarely addressed in recent research into power systems: 1) the necessity to validate operational constraints on OPF, which determine the feasibility of power systems designed for the OPF problems; and 2) and the necessity to developconventional methods for solving OPF problems which can be more effective than the commonly-used heuristic methods.

dc.publisherAmerican Institute of Mathematical Science
dc.titleEssential issues on solving optimal power flow problems using soft-computing
dc.typeJournal Article
dcterms.source.volume4
dcterms.source.number4
dcterms.source.startPage341
dcterms.source.endPage351
dcterms.source.issn2155-3289
dcterms.source.titleNumerical Algebra, Control and Optimization
curtin.note

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Numerical Algebra, Control and Optimization following peer review. Chan, K.Y. and Yu, C. and Teo, K.L. and Nordholm, S. 2014. Essential issues on solving optimal power flow problems using soft-computing. Numerical Algebra, Control and Optimization. 4 (4): pp. 341-351 is available online at: https://www.aimsciences.org/journals/contentsListnew.jsp?pubID=739

curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusOpen access


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