Posters

Abstract

Wind energy forms the largest share of net electricity generation from power plants for public electricity supply in Germany. The requirements from the perspective of performance monitoring and grid integration of offshore wind farms are becoming increasingly extensive and complex, due to significantly larger dimensions of offshore turbines and wind farms. For a reliable wind farm monitoring a precise real-time information about the wind conditions in and around the respective wind farm in order to be able to analyse the wind farm’s behaviour at any time and to enable technical and economic optimizations. In the three-year research project "Development of a lidar- and AI-supported method for large-scale measurement of the wind field inside and outside of offshore wind farms (WINDOW)”, which started in June 2021, such a real-time wind field will be developed. For this purpose, measurement data from different technologies (measuring mast, wind profile lidar, scanning lidar and wind turbine SCADA data) are combined and machine-learning methods are used to generate the wind field. As part of the project, two measurement campaigns are planned. The first takes place onshore and serves to develop methods. The second part is a case study in an offshore wind farm demonstrating the applications. The presented results summarise the methodical procedure including the use of an adaptive control of the scanning lidars and the measurement set-up and strategy of the first measurement campaign.