AI-based modeling of integrated floating offshore wind turbine and oscillating water column systems hybrid systems

• Autores: Irfan Ahmad, Fares Mzoughi, Payam Aboutalebi, Tahereh Bagheri Rouch, Aitor Josu Garrido Hernández, Izaskun Garrido Hernandez
• Localización: Haize eta Itsas Energiari Buruzko Irakaskuntza-Oharrak: PID2021-123543OB-C21 eta C22 proiektuen VI. Jardunaldiako Monografia / Aitor Josu Garrido Hernández (ed. lit.), Matilde Santos Peñas (ed. lit.), Izaskun Garrido Hernandez (ed. lit.), 2025, ISBN 978-84-09-70364-7, págs. 82-99
• Idioma: inglés
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• Resumen

  • Modeling complex dynamic systems is a criticaland often challenging step in developing advanced feedbackcontrol strategies, particularly for non-linear and uncertainenvironments. This paper presents a machine learning-based,control-oriented modeling framework designed to assistresearchers in accurately representing such systems with futurecontrol implementation in mind. The study focuses on theinteraction between Floating Offshore Wind Turbines (FOWTs)and Oscillating Water Columns (OWCs), two key componentsin marine renewable energy systems. Leveraging the predictivepower of Artificial Neural Networks (ANNs) and ConvolutionalNeural Networks (CNNs), the framework captures the intricatepitch dynamics of the platform, using empirical data from theFAST simulation environment. By integrating machine learningtechniques, the proposed framework offers a robust foundationfor developing high-fidelity models, facilitating the design ofeffective feedback control strategies for complex, non-linearsystems in marine environments