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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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This article presents a novel method to improve offshore surface wind speeds estimated by Synthetic Aperture Radar satellites and extrapolate them to higher altitudes. It can provide maps of the offshore extractible wind power up to 200 m and thus complement numerical models, especially in coastal areas. The method uses geometrical parameters of the sensor and parameters related to the atmospheric stability, combining them with machine learning. The final accuracy at 200 m is within 4 %.
Preprints
https://doi.org/10.5194/wes-2021-35
https://doi.org/10.5194/wes-2021-35

  10 May 2021

10 May 2021

Review status: this preprint is currently under review for the journal WES.

High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 SAR data and machine learning

Louis de Montera1, Henrick Berger1, Romain Husson1, Pascal Appelghem2, Laurent Guerlou1, and Mauricio Fragoso1 Louis de Montera et al.
  • 1CLS Collecte Localisation Satellites, Ramonville-Saint-Agne, France
  • 2Atmosky, Talence, France

Abstract. This paper presents a method to calculate offshore wind power at turbine hub height from Sentinel-1 Synthetic Aperture Radar (SAR) data using machine learning. The method is tested in two 70 km × 70 km areas off the Dutch coast where Lidar measurements are available. Firstly, SAR winds at surface level are improved with a machine learning algorithm using geometrical characteristics of the sensor and parameters related to the atmospheric stability extracted from a high-resolution numerical model. The wind speed bias at 10 m above sea level is reduced from −0.42 m s−1 to 0.02 m s−1 and its standard deviation from 1.41 m s−1 to 0.98 m s−1. After improvement, SAR surface winds are extrapolated at higher altitudes with a separate machine learning algorithm trained with the wind profiles measured by the Lidars. We show that, if profiling Lidars are available in the area of study, these two steps can be combined into a single one, in which the machine learning algorithm is trained directly at turbine hub height. Once the wind speed at turbine hub height is obtained, the extractible wind power is calculated using the method of the moments and a Weibull distribution. The results are given assuming an 8 MW turbine typical power curve. The accuracy of the wind power derived from SAR data is in the range ±3–4 % when compared with Lidars. Then, wind power maps at 200 m are presented and compared with the raw outputs of the numerical model at the same altitude. The maps based on SAR data have a much better level of detail, in particular regarding the coastal gradient. The new revealed patterns show differences with the numerical of as much as 10 % in some locations. We conclude that SAR data combined with a high-resolution numerical model and machine learning techniques can improve the wind power estimation at turbine hub height, and thus provide useful insights for optimizing wind farm siting and risk management.

Louis de Montera et al.

Status: open (until 21 Jun 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-35', Anonymous Referee #1, 11 May 2021 reply

Louis de Montera et al.

Louis de Montera et al.

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Short summary
This article presents a novel method to improve offshore surface wind speeds estimated by Synthetic Aperture Radar satellites and extrapolate them to higher altitudes. It can provide maps of the offshore extractible wind power up to 200 m and thus complement numerical models, especially in coastal areas. The method uses geometrical parameters of the sensor and parameters related to the atmospheric stability, combining them with machine learning. The final accuracy at 200 m is within 4 %.
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