The SWUF-3D Multicopter Fleet for Wind Energy Research
Keywords
drone, multicopter, wind, turbulence, wind energy, swarm
Presenter Abstract
The SWUF-3D fleet comprises 35 multicopter drones, optimized, calibrated, and validated for high-resolution three-dimensional measurements of wind, temperature, and humidity—enabling the capture of turbulent structures down to scales of a few meters. Coordinated fleet flights facilitate the measurement of spatial correlations, coherence, and advanced multi-point turbulence statistics. For the validation of wind turbine load and performance models, accurate inflow and wake characterization is essential but challenging with conventional in situ and remote sensing techniques. We present validation of the drone fleet’s measurements against a reference array of meteorological masts at the DLR research wind farm WiValdi. Lateral coherence and velocity increment statistics derived from the fleet data show strong agreement with those obtained from equally spaced sonic anemometers on the mast, within the limits of resolvable spatial scales. A key advantage of the drone fleet over stationary mast setups is its ability to enable flexible, three-dimensional measurement configurations, allowing simultaneous estimation of streamwise, lateral, and vertical coherence. The “cube pattern” is introduced as a representative example of such a configuration. Furthermore, the validated fleet was employed to measure wake flows in close proximity to wind turbines, revealing detailed structures such as wake turbulence, tip vortices, and swirl through distributed 3D wind data. When combined with lidar measurements, this integrated experimental setup enables a comprehensive quantification of the spatial distribution of turbulence across the wind farm.
Presentations
Presented in Session 4: New Observations I
The SWUF-3D Multicopter Fleet for Wind Energy Research
The SWUF-3D fleet comprises 35 multicopter drones, optimized, calibrated, and validated for high-resolution three-dimensional measurements of wind, temperature, and humidity—enabling the capture of turbulent structures down to scales of a few meters. Coordinated fleet flights facilitate the measurement of spatial correlations, coherence, and advanced multi-point turbulence statistics. For the validation of wind turbine load and performance models, accurate inflow and wake characterization is essential but challenging with conventional in situ and remote sensing techniques. We present validation of the drone fleet’s measurements against a reference array of meteorological masts at the DLR research wind farm WiValdi. Lateral coherence and velocity increment statistics derived from the fleet data show strong agreement with those obtained from equally spaced sonic anemometers on the mast, within the limits of resolvable spatial scales. A key advantage of the drone fleet over stationary mast setups is its ability to enable flexible, three-dimensional measurement configurations, allowing simultaneous estimation of streamwise, lateral, and vertical coherence. The “cube pattern” is introduced as a representative example of such a configuration. Furthermore, the validated fleet was employed to measure wake flows in close proximity to wind turbines, revealing detailed structures such as wake turbulence, tip vortices, and swirl through distributed 3D wind data. When combined with lidar measurements, this integrated experimental setup enables a comprehensive quantification of the spatial distribution of turbulence across the wind farm.