Exploratory Data Analysis ofWind and Waves for Floating Wind Turbines in Santa MaríaCalifornia

  1. Montserrat Sacie 1
  2. Rafael López 1
  3. Matilde Santos 1
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Libro:
Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings
  1. Cesar Analide (ed. lit.)
  2. Paulo Novais (ed. lit.)
  3. David Camacho (ed. lit.)
  4. Hujun Yin (ed. lit.)

Editorial: Springer International Publishing AG

ISBN: 978-3-030-62362-3,  978-3-030-62361-6,  978-3-030-62364-7,  978-3-030-62365-4

Año de publicación: 2020

Título del volumen: Part II

Volumen: 2

Páginas: 252-259

Congreso: Intelligent Data Engineering and Automated Learning – IDEAL (21. 2020. Guimarães)

Tipo: Aportación congreso

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

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.

Fuente de los datos: Dialnet