Detecting errors in wind turbines using machine learning and openFAST simulation

• Autores: Matilde Santos Peñas, Segundo Esteban San Román, B. Weiss
• Localización: WWME 2023 V. Jardunaldia - Itsas energiako sistemen aurrerapen berriei buruzko irakaskuntza-oharrak / Aitor J. Garrido Hernández (ed. lit.), Matilde Santos Peñas (ed. lit.), Izaskun Garrido Hernandez (ed. lit.), 2024, ISBN 978-84-09-58971-5, págs. 63-68
• Idioma: inglés
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• Resumen

  • This research explores advanced approaches to improve error detection and optimize performance in wind turbines in general and on barge-type floating offshore ones in particular.

    By integrating machine learning with OpenFAST simulation and utilizing MATLAB’s ThingSpeak as an IoT server, the study aims to provide real-time insights for offshore wind environments.

    The findings contribute to improved error detection strategies and refined maintenance practices, aligning with global efforts to enhance the efficiency and sustainability of offshore wind energy.