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Sensitivity analysis of GA parameters for ECED problem

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dc.citedby3
dc.contributor.authorKamil K.en_US
dc.contributor.authorRazali N.M.M.en_US
dc.contributor.authorTeh Y.Y.en_US
dc.contributor.authorid57195622807en_US
dc.contributor.authorid36440450000en_US
dc.contributor.authorid55786392300en_US
dc.date.accessioned2023-12-28T04:12:54Z
dc.date.available2023-12-28T04:12:54Z
dc.date.issued2013
dc.description.abstractTo meet the requirement of the regulations and to reduce the pollution to the targeted level, minimization of emission level has been added into the dispatch strategies of generators by formulating emission constrained economic dispatch (ECED). Besides the conventional method by using the Lagrange Multiplier several evolutionary computation techniques such as Genetic Algorithm, Particle Swarm Optimisation, Ant Colony and Differential Evolution have been gaining popularity in solving general economic dispatch problems due to their desirable characteristics such as non-gradient dependent and ability to search for global optima. The effectiveness of these stochastic search techniques however is heavily dependent on the genetic operators and their parameters. The paper presents the study on sensitivity analysis of the parameters of Genetic Algorithm (GA) for the ECED problem. The results discuss the range of parameters suitable to be employed for the optimization and compare the difference between conventional economic dispatch and the ECED solutions. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6564553
dc.identifier.doi10.1109/PEOCO.2013.6564553
dc.identifier.epage260
dc.identifier.scopus2-s2.0-84882779766
dc.identifier.spage256
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84882779766&doi=10.1109%2fPEOCO.2013.6564553&partnerID=40&md5=de28129bf514277f564f71d97f6e3de5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29401
dc.pagecount4
dc.sourceScopus
dc.sourcetitleProceedings of the 2013 IEEE 7th International Power Engineering and Optimization Conference, PEOCO 2013
dc.subjectGenetic algorithms
dc.subjectLagrange multipliers
dc.subjectParticle swarm optimization (PSO)
dc.subjectScheduling
dc.subjectConventional methods
dc.subjectDifferential Evolution
dc.subjectEconomic Dispatch
dc.subjectEconomic dispatch problems
dc.subjectEmission constrained economic dispatches
dc.subjectEvolutionary computation techniques
dc.subjectParticle swarm optimisation
dc.subjectStochastic search techniques
dc.subjectSensitivity analysis
dc.titleSensitivity analysis of GA parameters for ECED problemen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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