Artificial Neural Network Based Fault Diagnostic System for Wind Turbines

dc.contributor.authorYilmaz, Okan
dc.contributor.authorYuksel, Tolga
dc.date.accessioned2025-05-20T18:56:17Z
dc.date.issued2022
dc.departmentBilecik Şeyh Edebali Üniversitesi
dc.description30th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2022 -- Safranbolu, TURKEY
dc.description.abstractIncreasing global energy demand and decreasing energy resources have led to an increase in the use of renewable energy resources. In terms of continuity and accessibility of energy, wind energy has the largest share among these resources. The size of the energy demand also increases the turbine dimensions. Due to the growing turbine sizes and increasing electrical power, safety and efficiency factors, the system requires a detection structure against failures. In this study, fault detection was carried out in a three-bladed, horizontal axis, pitch-controlled, 4.8MW turbine. Various data gathered from the system are processed by a decision structure and it makes a decision about the system status. Input data, measured or obtained by various calculations, are used in fault diagnosis with artificial neural network(ANN).
dc.description.sponsorshipIEEE,IEEE Turkey Sect,Bahcesehir Univ
dc.identifier.doi10.1109/SIU55565.2022.9864803
dc.identifier.isbn978-1-6654-5092-8
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85138677582
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU55565.2022.9864803
dc.identifier.urihttps://hdl.handle.net/11552/7678
dc.identifier.wosWOS:001307163400142
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWoS
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWoS - Conference Proceedings Citation Index-Science
dc.language.isotr
dc.publisherIeee
dc.relation.ispartof2022 30th Signal Processing and Communications Applications Conference, Siu
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250518
dc.subjectWind turbine
dc.subjectfault diagnosis
dc.subjectartificial neural network
dc.titleArtificial Neural Network Based Fault Diagnostic System for Wind Turbines
dc.typeConference Object

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