Wind Tunnel Experimental Dataset for Wind Farm Control with Scanning Lidar Wake Measurements

Description This repository provides an open experimental dataset obtained from controlled wind tunnel experiments on a small wind farm composed of three scaled wind turbines. The measurements were conducted to support research on wind farm control, wake steering and turbine–turbine interactions. The dataset combines multiple measurement systems and acquisition rates in a single NetCDF structure, enabling reproducible research and model validation across a wide range of applications. Experimental scope The experiments were designed to investigate: closed-loop wind farm control strategies wake steering and yaw control wake propagation and recovery turbine interaction and farm-level performance validation of engineering and high-fidelity flow models The dataset enables benchmarking of wind farm control algorithms and validation of numerical simulations such as LES, actuator-line models and reduced-order models. Measurement systems included Wind tunnel reference measurements The dataset includes inflow reference signals from the wind tunnel, including Pitot-tube velocity, turntable angle (wind direction) and air density. Scanning lidar wake measurements Two scanning lidars were used to measure the wake flow field troughout the scaled cluster. The dataset provides: line-of-sight velocities from each lidar reconstructed velocity components measurement coordinates and average scan positions These measurements enable wake characterisation and model validation. Wind turbine operational and load data Each turbine includes synchronised measurements of: rotor speed and azimuth yaw angle and yaw demand blade pitch and pitch demand power production hub loads (yawing, nodding, torque) tower loads (fore-aft and side-side) Both high-frequency structural load measurements and lower-frequency SCADA-like signals are included. Sampling strategy The dataset combines two acquisition frequencies: high-frequency (2.5 KHz) turbine load signals (~10× higher sampling rate) lower-frequency (250 Hz) wind tunnel, lidar and turbine operational signals This reflects the real experimental acquisition system and allows multi-scale analysis of turbine and flow dynamics. Applications The dataset is intended for: wind farm control research wake modelling and validation system identification and control design benchmarking of LES and engineering wake models development of reduced-order models reproducible research and education File format All data are provided in NetCDF4 format to ensure: platform independence long-term accessibility compatibility with MATLAB, Python and scientific workflows License Creative Commons Attribution 4.0 (CC BY 4.0)