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Chaotic self-adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints

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dc.citedby22
dc.contributor.authorRajagopalan A.en_US
dc.contributor.authorKasinathan P.en_US
dc.contributor.authorNagarajan K.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorSengoden V.en_US
dc.contributor.authorAlavandar S.en_US
dc.contributor.authorid57194865787en_US
dc.contributor.authorid57194393495en_US
dc.contributor.authorid46161291900en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid56644822400en_US
dc.contributor.authorid23972273600en_US
dc.date.accessioned2023-05-29T07:24:27Z
dc.date.available2023-05-29T07:24:27Z
dc.date.issued2019
dc.descriptionGenetic algorithms; Learning algorithms; Multiobjective optimization; Particle swarm optimization (PSO); Combined economic emission dispatch; Multi-objective optimization problem; Performance metrics; Search Algorithms; Security constraint; Electric load dispatchingen_US
dc.description.abstractThe main goal behind the combined economic emission dispatch (CEED) is to reduce the costs incurred upon fuel and emission for the generating units available without any intention to violate the generator and security constraints. Hence, the CEED must be handled after considering two challenging goals such as the costs involved with emission and fuel. In this paper, chaotic self-adaptive interior search algorithm (CSAISA) was proposed to solve the CEED problems, considering the nonlinear behavior of generators in terms of valve point effects, prohibited operating zones, and security constraints. The proposed algorithm was tested for its effectiveness using 11-generating units (without security), IEEE-30 bus system, and IEEE-118 bus system with security constraints. The results of the proposed CSAISA were compared with interior search algorithm (ISA), harmony search algorithm (HSA), differential evolution (DE), particle swarm optimization (PSO), and genetic algorithm (GA). To conclude, the proposed CSAISA outperformed all other algorithms in terms of convergence speed, implementation time, and solution quality, which was tested using performance metrics. � 2019 John Wiley & Sons, Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNoe12026
dc.identifier.doi10.1002/2050-7038.12026
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85065659942
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85065659942&doi=10.1002%2f2050-7038.12026&partnerID=40&md5=19a0be85ce05cbf8e7f308c1c83c2eb3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24548
dc.identifier.volume29
dc.publisherJohn Wiley and Sons Ltden_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleInternational Transactions on Electrical Energy Systems
dc.titleChaotic self-adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraintsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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