Predicting Frequency, Time-To-Repair and Costs of Wind Turbine Failures

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dc.authorid0000-0002-5969-1534en_US
dc.contributor.authorÖztürk, Samet
dc.contributor.authorFthenakis, Vasilis
dc.date.accessioned2021-03-20T20:09:33Z
dc.date.available2021-03-20T20:09:33Z
dc.date.issued2020
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.description.abstractOperation and maintenance (O&M) costs, and associated uncertainty, for wind turbines (WTs) is a significant burden for wind farm operators. Many wind turbine failures are unpredictable while causing loss of energy production, and may also cause loss of asset. This study utilized 753 O&M event data from 21 wind turbines operating in Germany, to improve the prediction of failure frequency and associated costs. We applied Bayesian updating to predict wind turbine failure frequency and time-to-repair (TTR), in conjunction to machine learning techniques for assessing costs associated with failures. We found that time-to-failure (TTF), time-to-repair and the cost of failures depend on operational and environmental conditions. High elevation (>100 m) of the wind turbine installation was found to increase both the probability of failures and probability of delayed repairs. Furthermore, it was determined that direct-drive turbines are more favorable at locations with high capacity factor (more than 40%) whereas geared-drive turbines show lower failure costs than direct-drive ones at temperate-coastal locations with medium capacity factors (between 20% and 40%). Based on these findings, we developed a decision support tool that can guide a site-specific selection of wind turbine types, while providing a thorough estimation of O&M budgets.en_US
dc.description.sponsorshipTurkish Ministry of EducationTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK)en_US
dc.description.sponsorshipThis manuscript is part of S.O.'s PhD research that was funded by the Turkish Ministry of Education.en_US
dc.identifier.doi10.3390/en13051149en_US
dc.identifier.issn1996-1073
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttp://doi.org/10.3390/en13051149
dc.identifier.urihttps://hdl.handle.net/20.500.12885/464
dc.identifier.volume13en_US
dc.identifier.wosWOS:000524318700136en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÖztürk, Samet
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 turbineen_US
dc.subjectmaintenanceen_US
dc.subjectreliabilityen_US
dc.subjectBayesian updatingen_US
dc.subjectmachine learningen_US
dc.titlePredicting Frequency, Time-To-Repair and Costs of Wind Turbine Failuresen_US
dc.typeArticleen_US

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