SAR Surface Wind Estimation and Extrapolation at Turbine Hub Height with Machine Learning for Offshore Wind Farm Siting

Abstract

This paper presents a method to generate maps of offshore wind power at turbine hub height from spaceborne SAR data. Two techniques based on machine learning are presented. The first one can be trained with metocean buoys and the second one, more precise, requires on -site profiling Lidars. If Lidars are not available, SAR surface winds at 10m are improved with machine learning. They are then extrapolated at 40m with a classical power law, and then at higher altitudes with an atmospheric numerical model. If profiling Lidars are available, parameters from the numerical model are added as input to the machine learning algorithm and the training is performed directly at turbine hub height with the Lidar data. Once the wind at turbine hub height is obtained, the wind power is then calculated using a Weibull distribution. The resulting maps are compared with the outputs of the numerical model. The maps based on SAR data provide a much higher level of detail and a better estimation of the coastal gradient, which is important to optimize wind farm siting and available potential energy production. The accuracy of the wind power is found to be in the range ±5% compared to the Lidars.