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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.date2026-01-01
dc.date.accessioned2024-05-16T06:42:54Z
dc.date.available2024-05-16T06:42:54Z
dc.date.issued2024-01-01
dc.identifier.citation11th International Conference on Control, Mechatronics and Automation (ICCMA), Grimstad, Norway, 2023 : 346-351 (2024)es_ES
dc.identifier.urihttp://hdl.handle.net/10810/67974
dc.description.abstractHarnessing the power of wind and waves for renewable energy production has become vital in the quest for sustainable electricity generation. The fusion of Floating Offshore Wind Turbines (FOWTs) and Oscillating Water Columns (OWCs) has introduced a groundbreaking concept of hybrid offshore platforms, offering immense potential for energy absorption, reduced dynamic response, load mitigation, and improved cost efficiency. This study focuses on two primary objectives: firstly, the development of a regression-based modeling method for a hybrid aero-hydro-elastic-servo-mooring coupled numerical system, and secondly, the implementation of a customized fuzzybased control mechanism aimed at ensuring platform stability. To achieve these objectives, Artificial Neural Networks (ANNs) are employed as computational Machine Learning (ML) tools to accurately simulate the complex behavior of the hybrid system. The experimental results confirm the potential of ANN-based modeling as a simpler yet effective alternative to complicated nonlinear NREL-5MW FOWT dynamical models. Furthermore, the use of the FLC system enhances platform stability in a variety of wind and wave conditions.es_ES
dc.description.sponsorshipPID2021-123543OB-C21 and C22, funded by MCIN/AEI/10.13039/501100011033, as well as Basque Government project IT1555-22. The authors also acknowledge financial support from the UPV/EHU grant PIF20/299, the María Zambrano grant MAZAM22/15, and the Margarita Salas grant MARSA22/09, funded by the European Union-Next Generation EU.es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID2021-123543OB-C21es_ES
dc.relationinfo:eu-repo/grantAgreement/MICINN/PID2021-123543OB-C22es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjecthybrid offshore platformses_ES
dc.subjectload mitigationes_ES
dc.subjectregressionbased modelinges_ES
dc.subjectFuzzy Logic Controles_ES
dc.titleEnhancing Stability and Performance of Hybrid Offshore Wind Platforms: A Novel Fuzzy Logic Control Approach with Computational Machine Learninges_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.holder© 2023, IEEEes_ES
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/10374606es_ES
dc.identifier.doi10.1109/ICCMA59762.2023.10374606
dc.departamentoesIngeniería de sistemas y automáticaes_ES
dc.departamentoeuSistemen ingeniaritza eta automatikaes_ES


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