Supervised artificial neural networks by field-oriented control applied to PMSG-based variable-speed wind turbine

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dc.authorscopusidİlhami Çolak / 6602990030
dc.authorwosidİlhami Çolak / KVO-7460-2024
dc.contributor.authorAoumri, Mohammed
dc.contributor.authorHarrouz, Abdelkader
dc.contributor.authorYaichi, Ibrahim
dc.contributor.authorÇolak, İlhami
dc.contributor.authorKayışlı, Korhan
dc.contributor.authorDumbrava, Virgil
dc.date.accessioned2025-04-18T09:16:48Z
dc.date.available2025-04-18T09:16:48Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
dc.description.abstractArtificial intelligence techniques (AI), especially Artificial Neural Networks (ANN), have a significant impact on the control of electrical machines and power systems. Neural networks have created new and advanced boundaries in controlling power systems and electric generators, and they are already a complex, multidisciplinary technology undergoing dynamic evolution in recent years. This paper presents the application of a control methodology to a permanent magnet synchronous generator (PMSG) in the context of a variable speed wind turbine system. The study particularly focused on exploring the performance and optimization of the wind turbine system through the use of this control approach to extract the maximum amount of variable speed wind energy and address associated errors and problems, the control being studied and applied is Supervised ANN by FOC. The discussion explored the various components and performance of a wind system (WS) through simulations conducted in MATLAB/Simulink.
dc.identifier.citationAoumri, M., Harrouz, A., Yaichi, I., Colak, I., Kayisli, K., & Dumbrava, V. (2024, May). Supervised Artificial Neural Networks by Field-Oriented Control applied to PMSG-based Variable-Speed Wind Turbine. In 2024 12th International Conference on Smart Grid (icSmartGrid) (pp. 187-191). IEEE.
dc.identifier.doi10.1109/icSmartGrid61824.2024.10578216
dc.identifier.endpage191
dc.identifier.isbn979-835036161-2
dc.identifier.scopus2-s2.0-85199473390
dc.identifier.startpage187
dc.identifier.urihttp://dx.doi.org/10.1109/icSmartGrid61824.2024.10578216
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6743
dc.identifier.wosWOS:001266130300026
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorÇolak, İlhami
dc.institutionauthoridİlhami Çolak / 0000-0002-6405-5938
dc.language.isoen
dc.publisherInstitute of electrical and electronics engineers inc.
dc.relation.ispartof12th international conference on smart grid, icSmartGrid 2024
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Neural Networks (ANN)
dc.subjectField- Oriented Control (FOC)
dc.subjectPermanent Magnet Synchronous Generators (PMSG)
dc.subjectWind System
dc.titleSupervised artificial neural networks by field-oriented control applied to PMSG-based variable-speed wind turbine
dc.typeConference Object

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