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dc.contributor.authorAhmad, Irfan
dc.contributor.authorMzoughi, Fares
dc.contributor.authorAboutalebi, Payam
dc.contributor.authorGarrido Hernández, Izaskun ORCID
dc.contributor.authorGarrido Hernández, Aitor Josu ORCID
dc.date2025-02-20
dc.date.accessioned2024-05-16T14:14:49Z
dc.date.available2024-05-16T14:14:49Z
dc.date.issued2023-02-20
dc.identifier.citation26th International Conference on Circuits, Systems, Communications and Computers (CSCC), Crete, Greece, 2022 : 72-76 (2023)es_ES
dc.identifier.isbn978-1-6654-8187-8
dc.identifier.isbn978-1-6654-8186-1
dc.identifier.urihttp://hdl.handle.net/10810/67996
dc.description.abstractThe wind-wave excitations cause structural vibrations on the Floating Offshore Wind Turbines (FOWT) pressing the power generation efficiency and reducing the life expectancy. In particular, tower-top displacement and barge-type platform pitch dynamics are extremely sensitive to wind speed and wave elevation to the point that may even lead to structural instability in extreme conditions. Having into account that computational techniques such as Artificial Neural Networks (ANNs) are widely used in artificial intelligence because of their strong predicting and forecasting capabilities, the aim of this article is to create a deep-layer ANN model that incorporates Oscillating Water Columns (OWCs) into the barge platform. This ANN model enables to address stability issues of the hybrid floating offshore wind platform. The proposed control-oriented model has been successfully validated to achieve adequate dynamic behavior and structural performance using FAST.es_ES
dc.description.sponsorshipBasque Government IT555-22, PID2021-123543OB-C21 and PID2021-123543OB-C22 (MCIN/AEI/10.13039/501100011033/FEDER, UE)es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID2021-123543OB-C21es_ES
dc.relationnfo:eu-repo/grantAgreement/MICINN/PID2021-123543OB-C22es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectartificial neural networkes_ES
dc.subjectbarge platformes_ES
dc.subjectfloating offshore wind turbinees_ES
dc.subjectoscillating water columnes_ES
dc.titleA Machine-Learning Approach for the Development of a FOWT Model Integrated with Four OWCses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.holder© 2022, IEEEes_ES
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/10017703es_ES
dc.identifier.doi10.1109/CSCC55931.2022.00023
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES


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