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2024
Meeting Abstract
Title
Dual-Doppler scanning lidar measurements of wakes in an offshore wind farm
Abstract
Scanning lidars are a powerful tool to accurately sense wind conditions over large distances up to about 10 kilometers. In offshore wind energy applications this ensures comprehensive coverage of atmospheric dynamics in and around wind farms. This contribution presents measurement results of a dual-Doppler scanning lidar campaign conducted from March to November 2023 in and around an operating wind farm in the German Bight and compares them to engineering wake models.
Two scanning lidars were positioned on transition pieces of two different turbines and were used for measuring the undisturbed inflow as well as a spatially highly resolved grid for the wakes behind the wind farm at hub height. Central to the campaign’s success was an in-house developed scanning lidar software, enabling customized scan patterns and a spatially and temporally synchronized steering of the laser beams. An innovative feature of this software is the adaptive campaign steering, which adjusts the measurement layout automatically based on the prevailing wind direction. This ensured that the wake could be consistently measured behind the wind farm, utilizing two synchronized scanning lidar devices in dual-Doppler mode to capture the spatiotemporal evolution of wake characteristics. Inside the park dual-Doppler PPI-scans showed to be an effective tool in capturing the spatiotemporal characteristics of wakes within the wind farm. Additionally, a detailed uncertainty assessment regarding the movement of the scanning lidars on the transition pieces through wind and wave induced turbine tilt was performed and a correction method was developed.
Regarding the wake extent at westerly winds, the mean wind speed deficits show the expected tendencies to decrease with distance from the windfarm and to increase with wind turbine density per area, when multiple turbines being positioned behind each other in flow direction. In the comparative analysis of wake models conducted with PyWake, utilizing the TurbOPark model a consistent overestimation of wake deficits at the grid points measured by the dual-Doppler scanning lidar is visible, while the Jensen model underestimates the deficits at those locations. This discrepancy highlights the need for further refinement of wake models to better match the lidar-measured data.
The findings offer promising insights into the optimization of offshore wind farm operation and showcase the potential of dual-Doppler scanning lidar systems in offshore wind energy applications.
Two scanning lidars were positioned on transition pieces of two different turbines and were used for measuring the undisturbed inflow as well as a spatially highly resolved grid for the wakes behind the wind farm at hub height. Central to the campaign’s success was an in-house developed scanning lidar software, enabling customized scan patterns and a spatially and temporally synchronized steering of the laser beams. An innovative feature of this software is the adaptive campaign steering, which adjusts the measurement layout automatically based on the prevailing wind direction. This ensured that the wake could be consistently measured behind the wind farm, utilizing two synchronized scanning lidar devices in dual-Doppler mode to capture the spatiotemporal evolution of wake characteristics. Inside the park dual-Doppler PPI-scans showed to be an effective tool in capturing the spatiotemporal characteristics of wakes within the wind farm. Additionally, a detailed uncertainty assessment regarding the movement of the scanning lidars on the transition pieces through wind and wave induced turbine tilt was performed and a correction method was developed.
Regarding the wake extent at westerly winds, the mean wind speed deficits show the expected tendencies to decrease with distance from the windfarm and to increase with wind turbine density per area, when multiple turbines being positioned behind each other in flow direction. In the comparative analysis of wake models conducted with PyWake, utilizing the TurbOPark model a consistent overestimation of wake deficits at the grid points measured by the dual-Doppler scanning lidar is visible, while the Jensen model underestimates the deficits at those locations. This discrepancy highlights the need for further refinement of wake models to better match the lidar-measured data.
The findings offer promising insights into the optimization of offshore wind farm operation and showcase the potential of dual-Doppler scanning lidar systems in offshore wind energy applications.
Author(s)