Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

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dc.authoridWu, Yueqi/0000-0002-9396-1673
dc.authoridMa, Xiandong/0000-0001-7363-9727
dc.authoridDjurovic, Sinisa/0000-0001-7700-6492
dc.authorwosidTarek, BERGHOUT/AAF-4921-2021
dc.contributor.authorBenbouzid, Mohamed
dc.contributor.authorBerghout, Tarek
dc.contributor.authorSarma, Nur
dc.contributor.authorDjurovic, Sinisa
dc.contributor.authorWu, Yueqi
dc.contributor.authorMa, Xiandong
dc.date.accessioned2021-12-01T18:50:47Z
dc.date.available2021-12-01T18:50:47Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractModern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.en_US
dc.identifier.doi10.3390/en14185967
dc.identifier.issn1996-1073
dc.identifier.issue18en_US
dc.identifier.scopus2-s2.0-85115361833en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.3390/en14185967
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10925
dc.identifier.volume14en_US
dc.identifier.wosWOS:000699404300001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofEnergiesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectwind turbinesen_US
dc.subjectcondition monitoringen_US
dc.subjectdiagnosisen_US
dc.subjectprognosisen_US
dc.subjectmachine learningen_US
dc.subjectdata miningen_US
dc.subjecthealth managementen_US
dc.subjectoperations and maintenanceen_US
dc.subjectBearing Fault-Diagnosisen_US
dc.subjectDeep Learning-Modelen_US
dc.subjectTurbine Gearboxen_US
dc.subjectBig-Dataen_US
dc.subjectScada Dataen_US
dc.subjectPredictive Maintenanceen_US
dc.subjectAcoustic-Emissionen_US
dc.subjectDamage Detectionen_US
dc.subjectIdentificationen_US
dc.subjectPerformanceen_US
dc.titleIntelligent Condition Monitoring of Wind Power Systems: State of the Art Reviewen_US
dc.typeReview Articleen_US

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