A novel intelligent approach for yaw position forecasting in wind energy systems

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dc.authoridCOLAK, ILHAMI/0000-0002-6405-5938
dc.contributor.authorYesilbudak, Mehmet
dc.contributor.authorSagiroglu, Seref
dc.contributor.authorColak, Ilhami
dc.date.accessioned2024-09-11T19:50:59Z
dc.date.available2024-09-11T19:50:59Z
dc.date.issued2015
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractYaw control systems orientate the rotor of a wind turbine into the wind direction, optimize the wind power generated by wind turbines and alleviate the mechanical stresses on a wind turbine. Regarding the advantages of yaw control systems, a k-nearest neighbor classifier (k-NN) has been developed in order to forecast the yaw position parameter at 10-min intervals in this study. Air temperature, atmosphere pressure, wind direction, wind speed, rotor speed and wind power parameters are used in 2, 3, 4, 5 and 6-dimensional input spaces. The forecasting model using Manhattan distance metric for k= 3 uncovered the roost accurate performance for atmosphere pressure, wind direction, wind speed and rotor speed inputs. However, the forecasting model using Euclidean distance metric for k= 1 brought out the most inconsistent results for atmosphere pressure and wind speed inputs. As a result of multi-tupled analyses, many feasible inferences were achieved for yaw position control systems. In addition, the yaw position forecasting model developed was compared with the persistence model and it surpassed the persistence model significantly in terms of the improvement percent. (C) 2015 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.ijepes.2015.01.030
dc.identifier.endpage413en_US
dc.identifier.issn0142-0615
dc.identifier.issn1879-3517
dc.identifier.scopus2-s2.0-84923226581en_US
dc.identifier.startpage406en_US
dc.identifier.urihttps://doi.org/10.1016/j.ijepes.2015.01.030
dc.identifier.urihttps://hdl.handle.net/11363/7718
dc.identifier.volume69en_US
dc.identifier.wosWOS:000351251200044en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofInternational Journal of Electrical Power & Energy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectYaw positionen_US
dc.subjectWind turbinesen_US
dc.subjectForecastingen_US
dc.subjectLazy learningen_US
dc.subjectMulti-tupled inputsen_US
dc.titleA novel intelligent approach for yaw position forecasting in wind energy systemsen_US
dc.typeArticleen_US

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