| 02 Jun 2026
Experimental wind tunnel dataset of wake and turbine measurements for wind farm control
Abstract. Open-access experimental datasets play a central role in validating and benchmarking numerical models used in wind energy and wind-farm control research. However, publicly available datasets providing time-resolved turbine loads, actuator commands, and inflow characterisation under controlled operation remain scarce.
This paper presents a new open-access experimental dataset from wind-tunnel experiments featuring actuated, instrumented, scaled wind turbine models. The database includes time-resolved measurements of tower-base and rotating-shaft moments, rotor speed, generated torque and power, blade pitch angles, nacelle yaw angle, and controller commands. The inflow conditions are described in terms of wind speed, wind direction, air density, and wake-flow measurements, enabling detailed analyses of turbine response and controller behaviour under consistent, repeatable conditions.
The experiments cover a wide range of wake-control strategies, including yaw-based wake steering, curtailment and derating, Helix control, dynamic yaw actuation, Pulse wake mixing, individual pitch control, and several combinations of these strategies. The simultaneous availability of actuator commands, measured turbine response, and time-resolved structural loads enables detailed investigation of the controller performance, load variability, and dynamic turbine behaviour induced by active wake-control strategies.
In addition to the experimental measurements, the dataset is complemented by numerical models of the experiments, providing a reproducible experimental-numerical benchmarking framework that enables dataset extension, sensitivity analyses, and systematic validation of control-oriented aeroelastic and wake-interaction models.
The dataset is intended to support the validation and benchmarking of numerical tools for wind-farm control, the assessment of fatigue-relevant loading under wake-control operations, and to strengthen community efforts to improve transparency, reproducibility, and model fidelity in wind-farm design and control research.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
The manuscript presents an open-access wind-tunnel dataset for wind-farm-control studies using scaled G1 turbine models. While the dataset itself may have some practical value, the manuscript is not sufficiently strong as a journal article. It reads mainly like a laboratory data manual, with lengthy descriptions of hardware, sensors, actuator specifications, calibration procedures, data channels, acquisition rates, post-processing steps, and numerical model files. Much of this information may be useful for repository documentation, but it does not constitute a scientific contribution.
The central weakness is that the manuscript does not critically establish the value, fidelity, or limits of the dataset. Scaled wind-tunnel wake-control experiments are subject to Reynolds-number mismatch, artificial inflow generation, limited atmospheric realism, actuator-bandwidth scaling, structural-scaling compromises, blockage effects, and simplified farm geometry. These limitations directly affect whether the data can meaningfully validate wind-farm-control models for full-scale turbines. The manuscript describes the apparatus in detail but gives too little critical assessment of what the dataset can and cannot validate.
The manuscript also blurs the boundaries among measurement, correction, and modeling. Several quantities depend on post-processing, sensor corrections, load transformations, imbalance removal, derived quantities, and numerical models. A credible benchmark dataset should make these distinctions central, namely, what is directly measured, what is inferred, what is corrected, and what is model-generated. Instead, the paper presents procedural details without a sufficiently rigorous assessment of uncertainty and sensitivity.
Overall, I do not recommend the publication of this article. The dataset may be appropriate for an institutional repository, a technical report, or a supplementary data release, but the manuscript does not provide sufficient scientific analysis, critical interpretation, or validation to justify publication as a journal article. I recommend that the paper be declined in its present form.