Abstract
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.
Highlights
Estimating the available offshore wind power at turbine hub height is a challenging problem due to the difficulty in measuring the wind profile in the boundary layer over the sea
In order to ensure a fair comparison with the numerical model, the same approach was applied to its outputs
If profiling Lidars are available, the machine learning algorithm can be trained directly at turbine hub height with geometrical parameters of the Synthetic Aperture Radar (SAR) sensor and parameters related to the atmospheric stability
Summary
Estimating the available offshore wind power at turbine hub height is a challenging problem due to the difficulty in measuring the wind profile in the boundary layer over the sea. Contrary to Lidars, spaceborne sensors have the advantage of sounding large areas with high spatial resolution They are not perfect: their revisit period is typically low (a couple of days for Sentinel-1 in Europe, for example), and they use indirect measurements by estimating the offshore surface wind from the sea state. The method 65 requires two separate random forest algorithms: the first one improves SAR winds at surface level and the second one extrapolates them at turbine hub height. The algorithm extrapolating the surface wind to higher altitudes only depends on parameters related to the atmospheric stability It can be trained with Lidar data as a reference and applied in other areas. The resulting maps are presented and compared with the output of the numerical model in order to estimate the benefit of using these methods compared with a state-of-the-art technique
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