13 Jan 2023
13 Jan 2023
Status: this preprint is currently under review for the journal WES.

Validation of an interpretable data-driven wake model using lidar measurements from a free-field wake steering experiment

Balthazar Arnoldus Maria Sengers, Gerald Steinfeld, Paul Hulsman, and Martin Kuehn Balthazar Arnoldus Maria Sengers et al.
  • ForWind, Carl von Ossietzky Universität Oldenburg, Institute of Physics, Küpkersweg 70, 26129 Oldenburg, Germany

Abstract. Data-driven wake models have recently shown a high accuracy in reproducing wake characteristics from numerical data sets. This study used wake measurements from a lidar-equipped commercial wind turbine and inflow measurements from a nearby met mast to validate an interpretable data-driven surrogate wake model. The trained data-driven model was then compared to a state-of-the-art analytical wake model. A multi-plane lidar measurement strategy captured the occurrence of the wake curl during yaw misalignment, which had not yet conclusively been observed in the free field. The comparison between the wake models showed that the available power estimations of a virtual turbine situated four rotor diameters downstream were significantly more accurate with the data-driven model than with the analytical model. The Mean Absolute Percentage Error was reduced by 19 % to 36 %, depending on the input variables used. Especially under turbine yaw misalignment and high vertical shear, the data-driven model performed better. Further analysis suggested that the accuracy of the data-driven model is hardly affected when using only SCADA data as input. The outcome of this study demonstrates the enormous potential of data-driven wake models.

Balthazar Arnoldus Maria Sengers et al.

Status: open (until 10 Feb 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2022-118', Anonymous Referee #1, 27 Jan 2023 reply
  • RC2: 'Comment on wes-2022-118', Anonymous Referee #2, 31 Jan 2023 reply

Balthazar Arnoldus Maria Sengers et al.

Model code and software

Model code Data-driven wAke steeRing surrogaTe model (DART) Sengers, B. A. M. and Zech, M.

Balthazar Arnoldus Maria Sengers et al.


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Short summary
The optimal misalignment angles for wake steering are determined using wake models. Although mostly analytical, data-driven models have recently shown promising results. This study validates a previously proposed data-driven model with results from a field experiment using lidar measurements. In a comparison with a state-of-the-art analytical model, it shows systematically more accurate estimates of the available power. Also when using only commonly available data, it provides good results.