Comparative performance of twelve metaheuristics for wind farm layout optimisation

dc.contributor.authorKunakote, Tawatchai
dc.contributor.authorSabangban, Numchoak
dc.contributor.authorTejani, Ghanshyam G.
dc.contributor.authorPanagant, Natee
dc.contributor.authorPholdee, Nantiwat
dc.contributor.authorKumar, Sumit
dc.contributor.buuauthorYıldız, Ali Rıza
dc.contributor.departmentUludağ Üniversitesi/Mühendislik Fakültesi/Makina Mühendisliği
dc.contributor.orcid0000-0003-1790-6987
dc.contributor.researcheridF-7426-2011
dc.contributor.scopusid7102365439
dc.date.accessioned2024-05-24T10:29:39Z
dc.date.available2024-05-24T10:29:39Z
dc.date.issued2022-01
dc.description.abstractThis work bridges two research fields i.e. metaheuristics and wind farm layout design. Comparative performance of twelve metaheuristics (MHs) on wind farm layout optimisation (WFLO) was conducted. Four WFLO problems are proposed for benchmarking the various metaheuristics while the design problem is an attempt to simultaneously minimise wind farm cost and maximise wind farm totally produced power. Design variables are wind turbine placement with fixed and varied number of wind turbines. The Jansen's wake model is used while two types of energy estimation with and without considering partially overshadowed wake areas are studied. The results obtained from using various MHs are statistically compared in terms of convergence and consistency while the best performer is obtained. Comparison results indicated that moth-flame optimisation (MFO) algorithm is the most efficient algorithms. The results obtained in this work are said to be the baseline for future study on WFLO using metahueristics.
dc.description.sponsorshipThailand Research Fund (TRF) (RTA6180010)
dc.identifier.doihttps://doi.org/10.1007/s11831-021-09586-7
dc.identifier.endpage730
dc.identifier.issn1134-3060
dc.identifier.issn1886-1784
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85104822427
dc.identifier.startpage717
dc.identifier.urihttps://link.springer.com/article/10.1007/s11831-021-09586-7
dc.identifier.urihttps://hdl.handle.net/11452/41523
dc.identifier.volume29
dc.identifier.wos000640939700001
dc.indexed.pubmed
dc.indexed.scopusScopus
dc.indexed.wosSCIE
dc.language.isoen
dc.publisherSpringer
dc.relation.collaborationYurt dışı
dc.relation.journalArchives of Computational Methods in Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectLearning-based optimization
dc.subjectDifferential evolution
dc.subjectGenetic algorithm
dc.subjectTurbines
dc.subjectDesign
dc.subjectSelection
dc.subjectModel
dc.subject.scopusWind Turbine; Genetic Algorithm; Electric Utility
dc.subject.wosComputer science, interdisciplinary applications
dc.subject.wosEngineering, multidisciplinary
dc.subject.wosMathematics, interdisciplinary applications
dc.titleComparative performance of twelve metaheuristics for wind farm layout optimisation
dc.typeReview

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