Proportional-integral-derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm

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dc.contributor.authorCivelek, Zafer
dc.contributor.authorCam, Ertugrul
dc.contributor.authorLuy, Murat
dc.contributor.authorMamur, Hayati
dc.date.accessioned2020-06-25T18:16:16Z
dc.date.available2020-06-25T18:16:16Z
dc.date.issued2016
dc.departmentKırıkkale Üniversitesi
dc.descriptionLUY, Murat/0000-0002-2378-0009; Cam, Ertugrul/0000-0001-6491-9225
dc.description.abstractOutput powers of wind turbines (WTs) with variable blade pitch over nominal wind speeds are controlled by means of blade pitch adjustment. While tuning the blade pitch, conventional proportional-integral-derivative (PID) controllers and some intelligent genetic algorithms (IGAs) are widely used in hot systems. Since IGAs are community-based optimisation methods, they have an ability to look for multi-point solutions. However, the PID parameter setting optimisation of the IGA controllers is important and quite difficult a step in WTs. To solve this problem, while the optimisation is carried out by regulating mutation rates in some IGA controllers, the optimisation is conducted by altering crossover point numbers in others. In this study, a new IGA algorithm approach has been suggested for the PID parameter setting optimisation of the blade pitch controller. The algorithm rearranging both the mutation rate and the crossover point number together according to the algorithm progress has been firstly used. The new IGA approach has also been tested and validated by using MATLAB/Simulink software. Then, its superiority has been proved by comparing the other genetic algorithm (GAs). Consequently, the new IGA approach has more successfully adjusted the blade pitch of a WT running at higher wind speeds than other GA methods.en_US
dc.description.sponsorshipScientific and Technological Research Council of TURKEY (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [114E419]en_US
dc.description.sponsorshipThis project was supported by the Scientific and Technological Research Council of TURKEY (TUBITAK) under Grant 114E419 ('Design, optimization and experimental verification of a permanent magnet synchronous generator with external rotor, maximum power and minimum cogging torque').en_US
dc.identifier.citationCivelek, Z., Cam, E., Luy, M. ve Mamur, H. (2016). Proportional-integral-derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm. IET RENEWABLE POWER GENERATION, 10(8), 1220–1228.en_US
dc.identifier.doi10.1049/iet-rpg.2016.0029
dc.identifier.endpage1228en_US
dc.identifier.issn1752-1416
dc.identifier.issn1752-1424
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-84986903235
dc.identifier.scopusqualityQ2
dc.identifier.startpage1220en_US
dc.identifier.urihttps://doi.org/10.1049/iet-rpg.2016.0029
dc.identifier.urihttps://hdl.handle.net/20.500.12587/6473
dc.identifier.volume10en_US
dc.identifier.wosWOS:000384016100020
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInst Engineering Technology-Ieten_US
dc.relation.ispartofIET Renewable Power Generation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectthree-term controlen_US
dc.subjectgenetic algorithmsen_US
dc.subjectwind turbinesen_US
dc.subjectpower generation controlen_US
dc.subjectproportional-integral-derivative parameter optimisationen_US
dc.subjectblade pitch controlleren_US
dc.subjectwind turbinesen_US
dc.subjectintelligent genetic algorithmen_US
dc.subjectPID controllersen_US
dc.subjecthot systemsen_US
dc.subjectcommunity-based optimisation methodsen_US
dc.subjectPID parameter setting optimisationen_US
dc.subjectIGA controllersen_US
dc.subjectWTen_US
dc.subjectmutation rateen_US
dc.subjectMATLABen_US
dc.subjectSimulink softwareen_US
dc.titleProportional-integral-derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithmen_US
dc.typeArticle

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