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An artificial intelligent approach for the optimization of organic rankine cycle power generation systems

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dc.citedby5
dc.contributor.authorTan J.D.en_US
dc.contributor.authorLim C.W.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorKoay Y.Y.en_US
dc.contributor.authorid38863172300en_US
dc.contributor.authorid35722335000en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid57189626122en_US
dc.date.accessioned2023-05-29T07:26:22Z
dc.date.available2023-05-29T07:26:22Z
dc.date.issued2019
dc.description.abstractThe study on Organic Rankine Cycle (ORC) power generation system has gained significant popularity among researchers over the past decade, mainly due to the financial and environmental benefits that the system provides. A good maximum power point tracking (MPPT) mechanism can push the efficiency of an ORC to a higher rate. In this research, a Self-Adjusted Peak Search algorithm (SAPS) is proposed as the MPPT scheme of an ORC system. The SAPS has the ability to perform a relatively detailed search when the convergence reaches the near-optima peak without jeopardizing the speed of the overall convergence process. The SAPS is tested in a simulation to track for a moving maximum power pint (MPP) of an ORC system. Experiment results show that the SAPS outperformed several other well-established optimization algorithm in tracking the moving MPP, especially in term of the solution accuracies. It can thus be concluded that the proposed SAPS performs well as a mean of an MPPT scheme in an ORC system. � 2019 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijeecs.v14.i1.pp340-345
dc.identifier.epage345
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85061148782
dc.identifier.spage340
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85061148782&doi=10.11591%2fijeecs.v14.i1.pp340-345&partnerID=40&md5=0dea1f309e7bf975d8736e4eed8d8503
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24732
dc.identifier.volume14
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Hybrid Gold
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Computer Science
dc.titleAn artificial intelligent approach for the optimization of organic rankine cycle power generation systemsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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