A novel hybrid metaheuristic optimization method to estimate medium-term output power for horizontal axis wind turbine

Basit Öğe Kaydı
dc.authoridBilgili, Mehmet/0000-0002-5339-6120
dc.authoridekinci, firat/0000-0002-4888-7881
dc.authoridAksu, Inayet Ozge/0000-0002-0963-2982
dc.authoridAygul, Kemal/0000-0002-7840-5441
dc.authoridDemirdelen, Tugce/0000-0002-1602-7262
dc.authoridESENBOGA, BURAK/0000-0002-7777-259X
dc.contributor.authorEkinci, Firat
dc.contributor.authorDemirdelen, Tugce
dc.contributor.authorAksu, Inayet Ozge
dc.contributor.authorAygul, Kemal
dc.contributor.authorEsenboga, Burak
dc.contributor.authorBilgili, Mehmet
dc.date.accessioned2025-01-06T17:37:39Z
dc.date.available2025-01-06T17:37:39Z
dc.date.issued2019
dc.description.abstractThe increasing damage caused by fossil fuels has made it a necessity for new and clean energy sources. In recent years, the use of wind energy from renewable energy sources has increased, which is a new and clean energy source. Wind energy is everywhere in nature. The wind speed changes depending on time. Thus, the wind power is unstable. In order to keep this disadvantage at a minimum level, future power estimation studies have been carried out. In these studies, different methods and algorithms are applied to estimate short and medium term in wind power. In this study, artificial neural network, particle swarm optimization and firefly algorithm (FA) as a new method are used for the first time in predicting wind power. As input data, temperature, wind speed and rotor speed the data recorded in the SCADA in wind turbines are used to predict medium-term wind speed and also wind power. Each method is compared in detail and their performances are revealed.
dc.description.sponsorshipScientific Project Unit of Adana Science and Technology University [18103015, 18103016]
dc.description.sponsorshipThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to acknowledge the Scientific Project Unit of Adana Science and Technology University (Project Number: 18103015 and 18103016) for full financial support.
dc.identifier.doi10.1177/0957650918821040
dc.identifier.endpage658
dc.identifier.issn0957-6509
dc.identifier.issn2041-2967
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85060704896
dc.identifier.scopusqualityQ2
dc.identifier.startpage646
dc.identifier.urihttps://doi.org/10.1177/0957650918821040
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2317
dc.identifier.volume233
dc.identifier.wosWOS:000480249800008
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSage Publications Ltd
dc.relation.ispartofProceedings of The Institution of Mechanical Engineers Part A-Journal of Power and Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectEngineering optimization
dc.subjectwind power prediction
dc.subjectartificial neural network
dc.subjectmetaheuristic optimization
dc.subjectfirefly algorithms
dc.titleA novel hybrid metaheuristic optimization method to estimate medium-term output power for horizontal axis wind turbine
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu