Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7518
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dc.contributor.authorSimani, Silvio-
dc.contributor.authorTurhan, Cihan-
dc.date.accessioned2019-12-24T07:47:57Z
dc.date.available2019-12-24T07:47:57Z
dc.date.issued2018en_US
dc.identifier.citationSimani, S., and Turhan, C. (2018). Fault diagnosis of a wind turbine simulated model via neural networks. IFAC-PapersOnLine, 51(24), 381-388. doi:10.1016/j.ifacol.2018.09.605en_US
dc.identifier.issn2405-8963
dc.identifier.issn2405-8963-
dc.identifier.urihttps://doi.org/10.1016/j.ifacol.2018.09.605
dc.identifier.urihttps://hdl.handle.net/11147/7518
dc.description.abstractThe fault diagnosis of wind turbine systems has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of wind turbines, and it proposes viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves a data-driven approach, as it represents an effective tool for coping with a poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the data-driven proposed solution relies on neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen network architecture belongs to the nonlinear autoregressive with exogenous input topology, as it can represent a dynamic evolution of the system along time. The developed fault diagnosis scheme is tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are compared with those of other control strategies, coming from the related literature. Moreover, a Monte Carlo analysis validates the robustness of the proposed solutions against the typical parameter uncertainties and disturbances.en_US
dc.language.isoenen_US
dc.publisherIFAC Secretariaten_US
dc.relation.ispartofIFAC-PapersOnLineen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFault diagnosisen_US
dc.subjectFault estimationen_US
dc.subjectWind turbinesen_US
dc.subjectNeural networksen_US
dc.subjectRobustness and reliabilityen_US
dc.titleFault diagnosis of a wind turbine simulated model via neural networksen_US
dc.typeArticleen_US
dc.institutionauthorTurhan, Cihan-
dc.departmentİzmir Institute of Technology. Mechanical Engineeringen_US
dc.identifier.volume51en_US
dc.identifier.issue24en_US
dc.identifier.startpage381en_US
dc.identifier.endpage388en_US
dc.identifier.wosWOS:000447016900056en_US
dc.identifier.scopus2-s2.0-85054585991en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1016/j.ifacol.2018.09.605-
dc.relation.doi10.1016/j.ifacol.2018.09.605en_US
dc.coverage.doi10.1016/j.ifacol.2018.09.605en_US
dc.identifier.scopusqualityQ3-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
Appears in Collections:Mechanical Engineering / Makina Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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