Exploratory Data Analysis ofWind and Waves for Floating Wind Turbines in Santa María: California

• Montserrat Sacie ^[1] ; Rafael López ^[1] ; Matilde Santos ^[1]
  1. [1] Universidad Complutense de Madrid

     Universidad Complutense de Madrid

     Madrid, España

     
• Localización: Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings / Cesar Analide (ed. lit.), Paulo Novais (ed. lit.), David Camacho Fernández (ed. lit.), Hujun Yin (ed. lit.), Vol. 2, 2020 (Part II), ISBN 978-3-030-62365-4, págs. 252-259
• Idioma: inglés
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• Resumen

  • Offshore wind turbines, and particularly floating wind turbines (FOWT) are subjected to strong wind and wave loads that affect the structural stability and energy efficiency of these renewable energy devices. Although wind -and less often waves- forecasting models have been developed, a deep analysis of the relationship between both external disturbances is necessary to consider the combined effect on the fatigue of the offshore WT. This work presents a study of the most relevant features of wind and waves using distribution analysis and ML techniques on wind and waves real data from an offshore buoy. Linear regression and SVM have been applied to the modelling of the data. These models may be very useful for the design of these floating structures and to study the impact of these external loads on the fatigue. The results lead us to consider the necessity of generating short-term models in specific geographical locations.