Articles | Volume 11, issue 8
https://doi.org/10.5194/wes-11-2817-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-2817-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Aug 2026
Research article |
| 05 Aug 2026
| 05 Aug 2026
Multi-strategy wind farm control: alternating wake steering and helix wake mixing on a large-scale wind farm
Faculty of Mechanical Engineering, Delft University of Technology, Delft, the Netherlands
Daan van der Hoek
Faculty of Mechanical Engineering, Delft University of Technology, Delft, the Netherlands
Tim Dammann
Faculty of Mechanical Engineering, Delft University of Technology, Delft, the Netherlands
Pieter M. O. Gebraad
Youwind, Barcelona, Spain
Jenna Iori
Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Jan-Willem van Wingerden
Faculty of Mechanical Engineering, Delft University of Technology, Delft, the Netherlands
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optimalexcitation frequency is heavily platform dependent.
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(co-design).
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In this paper we present a revised dynamic control-oriented wind farm model. The model can simulate turbine wake behaviour in heterogeneous and changing wind conditions at a very low computational cost. It utilizes a three-dimensional turbine wake model which also allows capturing vertical wind speed differences. The model could be used to maximise the power generation of with farms, even during events like a wind direction change. It is publicly available and open for further development.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
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The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
Daan van der Hoek, Joeri Frederik, Ming Huang, Fulvio Scarano, Carlos Simao Ferreira, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 1305–1320, https://doi.org/10.5194/wes-7-1305-2022, https://doi.org/10.5194/wes-7-1305-2022, 2022
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The paper presents a wind tunnel experiment where dynamic induction control was implemented on a small-scale turbine. By periodically changing the pitch angle of the blades, the low-velocity turbine wake is perturbed, and hence it recovers at a faster rate. Small particles were released in the flow and subsequently recorded with a set of high-speed cameras. This allowed us to reconstruct the flow behind the turbine and investigate the effect of dynamic induction control on the wake.
Yichao Liu, Riccardo Ferrari, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 523–537, https://doi.org/10.5194/wes-7-523-2022, https://doi.org/10.5194/wes-7-523-2022, 2022
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The objective of the paper is to develop a data-driven output-constrained individual pitch control approach, which will not only mitigate the blade loads but also reduce the pitch activities. This is achieved by only reducing the blade loads violating a user-defined bound, which leads to an economically viable load control strategy. The proposed control strategy shows promising results of load reduction in the wake-rotor overlapping and turbulent sheared wind conditions.
Unai Gutierrez Santiago, Alfredo Fernández Sisón, Henk Polinder, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 505–521, https://doi.org/10.5194/wes-7-505-2022, https://doi.org/10.5194/wes-7-505-2022, 2022
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The gearbox is one of the main contributors to the overall cost of wind energy, and it is acknowledged that we still do not fully understand its loading. The study presented in this paper develops a new alternative method to measure input rotor torque in wind turbine gearboxes, overcoming the drawbacks related to measuring on a rotating shaft. The method presented in this paper could make measuring gearbox torque more cost-effective, which would facilitate its adoption in serial wind turbines.
Aemilius A. W. van Vondelen, Sachin T. Navalkar, Alexandros Iliopoulos, Daan C. van der Hoek, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 161–184, https://doi.org/10.5194/wes-7-161-2022, https://doi.org/10.5194/wes-7-161-2022, 2022
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The damping of an offshore wind turbine is a difficult physical quantity to predict, although it plays a major role in a cost-effective turbine design. This paper presents a review of all approaches that can be used for damping estimation directly from operational wind turbine data. As each use case is different, a novel suitability table is presented to enable the user to choose the most appropriate approach for the given availability and characteristics of measurement data.
Cited articles
Anand, A., Braunbehrens, R., Guilloré, A., and Bottasso, C. L.: Economic lifetime-aware wind farm control, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2025-67, in review, 2025. a
Baricchio, M.: matteobaricchi/msr: First release – code and data (2) (version 1.1.2), Zenodo [code and data set], https://doi.org/10.5281/zenodo.21669959, 2026. a
Bechmann, A. and Quick, J.: Market-Driven Wind Resource Assessment, in: WindEurope Annual Event 2025, no. 1 in: J. Phys. Conf. Ser., IOP Publishing, United Kingdom, windEurope Annual Event 2025, 8 tp 10 April 2025, WindEurope, https://doi.org/10.1088/1742-6596/3025/1/012001, 2025. a
Becker, M., Lejeune, M., Chatelain, P., Allaerts, D., Mudafort, R., and van Wingerden, J.-W.: A dynamic open-source model to investigate wake dynamics in response to wind farm flow control strategies, Wind Energ. Sci., 10, 1055–1075, https://doi.org/10.5194/wes-10-1055-2025, 2025a. a
Becker, M., van den Broek, M. J., Allaerts, D., and van Wingerden, J. W.: Closed-loop model-predictive wind farm flow control under time-varying inflow using FLORIDyn, Wind Energy, 28, https://doi.org/10.1002/we.70044, 2025b. a
Brown, K., Yalla, G., Cheung, L., Frederik, J., Houck, D., deVelder, N., Simley, E., and Fleming, P.: Comparison of wind-farm control strategies under realistic offshore wind conditions: wake quantities of interest, Wind Energ. Sci., 10, 1737–1762, https://doi.org/10.5194/wes-10-1737-2025, 2025. a, b
Campagnolo, F., Weber, R., Schreiber, J., and Bottasso, C. L.: Wind tunnel testing of wake steering with dynamic wind direction changes, Wind Energ. Sci., 5, 1273–1295, https://doi.org/10.5194/wes-5-1273-2020, 2020. a
Dammann, T., van der Hoek, D., Yu, W., and van Wingerden, J. W.: Enhanced Wind Farm Performance via Active Wake Control: A Steady-State Approach, in: 2025 American Control Conference (ACC), 2856–2861, https://doi.org/10.23919/ACC63710.2025.11107695, 2025. a
Dammann, T., van der Hoek, D., Yu, W., and van Wingerden, J. W.: A novel engineering wake model for helix-actuated wind turbine wakes, Appl. Energ., 414, 127808, https://doi.org/10.1016/j.apenergy.2026.127808, 2026a. a
Dammann, T., Yu, W., and Wingerden, J.-W. v.: Balancing Actuation Penalties and Wake Recovery: The Efficacy of the Helix in Turbulent Inflows, J. Phys. Conf. Ser., 3224, 032002, https://doi.org/10.1088/1742-6596/3224/3/032002, 2026b. a
Doekemeijer, B. M., Kern, S., Maturu, S., Kanev, S., Salbert, B., Schreiber, J., Campagnolo, F., Bottasso, C. L., Schuler, S., Wilts, F., Neumann, T., Potenza, G., Calabretta, F., Fioretti, F., and van Wingerden, J.-W.: Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy, Wind Energ. Sci., 6, 159–176, https://doi.org/10.5194/wes-6-159-2021, 2021. a, b
Fleming, P. A., Gebraad, P. M., Lee, S., van Wingerden, J. W., Johnson, K., Churchfield, M., Michalakes, J., Spalart, P., and Moriarty, P.: Evaluating techniques for redirecting turbine wakes using SOWFA, Renew. Energ., 70, 211–218, https://doi.org/10.1016/j.renene.2014.02.015, 2014. a
Fleming, P. A., Stanley, A. P. J., Bay, C. J., King, J., Simley, E., Doekemeijer, B. M., and Mudafort, R.: Serial-Refine Method for Fast Wake-Steering Yaw Optimization, J. Phys. Conf. Ser., 2265, 032109, https://doi.org/10.1088/1742-6596/2265/3/032109, 2022. a, b
Frederik, J. A. and van Wingerden, J. W.: On the load impact of dynamic wind farm wake mixing strategies, Renew. Energ., 194, 582–595, https://doi.org/10.1016/j.renene.2022.05.110, 2022. a, b, c, d
Frederik, J. A., Doekemeijer, B. M., Mulders, S. P., and van Wingerden, J. W.: The helix approach: Using dynamic individual pitch control to enhance wake mixing in wind farms, Wind Energy, 23, 1739–1751, https://doi.org/10.1002/we.2513, 2020. a, b
Frederik, J. A., Simley, E., Brown, K. A., Yalla, G. R., Cheung, L. C., and Fleming, P. A.: Comparison of wind farm control strategies under realistic offshore wind conditions: turbine quantities of interest, Wind Energ. Sci., 10, 755–777, https://doi.org/10.5194/wes-10-755-2025, 2025. a, b, c
Gaumond, M., Réthoré, P.-E., Ott, S., Peña, A., Bechmann, A., and Hansen, K. S.: Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm, Wind Energy, 17, 1169–1178, https://doi.org/10.1002/we.1625, 2014. a
Gebraad, P. M. O., Teeuwisse, F. W., van Wingerden, J. W., Fleming, P. A., Ruben, S. D., Marden, J. R., and Pao, L. Y.: Wind plant power optimization through yaw control using a parametric model for wake effects – a CFD simulation study, Wind Energy, 19, 95–114, https://doi.org/10.1002/we.1822, 2016. a
Gray, J. S., Hwang, J. T., Martins, J. R. R. A., Moore, K. T., and Naylor, B. A.: OpenMDAO: An open-source framework for multidisciplinary design, analysis, and optimization, Struct. Multidiscip. O., 59, 1075–1104, https://doi.org/10.1007/s00158-019-02211-z, 2019. a
Guilloré, A., Campagnolo, F., and Bottasso, C. L.: A control-oriented load surrogate model based on sector-averaged inflow quantities: capturing damage for unwaked, waked, wake-steering and curtailed wind turbines, J. Phys. Conf. Ser., 2767, 032019, https://doi.org/10.1088/1742-6596/2767/3/032019, 2024. a
Hodgson, E. L. and Andersen, S. J.: Wake steering under inflow wind direction uncertainty: an LES study, Wind Energ. Sci., 11, 2173–2190, https://doi.org/10.5194/wes-11-2173-2026, 2026. a
Jimenez, A., Crespo, A., and Migoya, E.: Application of a LES technique to characterize the wake deflection of a wind turbine in yaw, Wind Energy, 13, 559–572, https://doi.org/10.1002/we.380, 2010. a
Kainz, S., Scherzl, A., Guilloré, A., Anand, A., and Bottasso, C. L.: An initial study on the environmental value of wind farm control, J. Phys. Conf. Ser., 3131, 012044, https://doi.org/10.1088/1742-6596/3131/1/012044, 2025. a
Kuhn, M. B., Henry de Frahan, M. T., Mohan, P., Deskos, G., Churchfield, M., Cheung, L., Sharma, A., Almgren, A., Ananthan, S., Brazell, M. J., A., M. L., Thedin, R., Rood, J., Sakievich, P., Vijayakumar, G., Zhang, W., and Sprague, M. A.: AMR-Wind: A performance-portable, high-fidelity flow solver for wind farm simulations, Wind Energy, 28, https://doi.org/10.1002/we.70010, 2025. a
Liew, J., Urbán, A. M., and Andersen, S. J.: Analytical model for the power–yaw sensitivity of wind turbines operating in full wake, Wind Energ. Sci., 5, 427–437, https://doi.org/10.5194/wes-5-427-2020, 2020. a, b
Mittelmeier, N., Blodau, T., and Kühn, M.: Monitoring offshore wind farm power performance with SCADA data and an advanced wake model, Wind Energ. Sci., 2, 175–187, https://doi.org/10.5194/wes-2-175-2017, 2017. a
Mühle, F. V., Heckmeier, F. M., Campagnolo, F., and Breitsamter, C.: Wind tunnel investigations of an individual pitch control strategy for wind farm power optimization, Wind Energ. Sci., 9, 1251–1271, https://doi.org/10.5194/wes-9-1251-2024, 2024. a
Munters, W. and Meyers, J.: Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and sinusoidal induction control of first-row turbines, Wind Energ. Sci., 3, 409–425, https://doi.org/10.5194/wes-3-409-2018, 2018. a
National Renewable Energy Laboratory: OpenFAST: An Open-Source Wind Turbine Simulation Tool, https://github.com/OpenFAST/openfast (last access: 1 November 2024), version 3.5.0, 2024b. a
Pedersen, J. G., Svensson, E., Poulsen, L., and Nygaard, N. G.: Turbulence Optimized Park model with Gaussian wake profile, J. Phys. Conf. Ser., 2265, 022063, https://doi.org/10.1088/1742-6596/2265/2/022063, 2022. a
Pedersen, M. M., Forsting, A. M., van der Laan, P., Riva, R., Romàn, L. A. A., Risco, J. C., Friis-Møller, M., Quick, J., Christiansen, J. P. S., Rodrigues, R. V., Olsen, B. T., and Réthoré, P.-E.: PyWake 2.5.0: An open-source wind farm simulation tool, https://gitlab.windenergy.dtu.dk/TOPFARM/PyWake (last access: 1 November 2024), 2023. a, b
Quick, J., Annoni, J., King, R., Dykes, K., Fleming, P., and Ning, A.: Optimization Under Uncertainty for Wake Steering Strategies, J. Phys. Conf. Ser., 854, 012036, https://doi.org/10.1088/1742-6596/854/1/012036, 2017. a, b, c
Quick, J., King, J., King, R. N., Hamlington, P. E., and Dykes, K.: Wake steering optimization under uncertainty, Wind Energ. Sci., 5, 413–426, https://doi.org/10.5194/wes-5-413-2020, 2020. a
Rott, A., Doekemeijer, B., Seifert, J. K., van Wingerden, J.-W., and Kühn, M.: Robust active wake control in consideration of wind direction variability and uncertainty, Wind Energ. Sci., 3, 869–882, https://doi.org/10.5194/wes-3-869-2018, 2018. a, b
Simley, E., Fleming, P., and King, J.: Design and analysis of a wake steering controller with wind direction variability, Wind Energ. Sci., 5, 451–468, https://doi.org/10.5194/wes-5-451-2020, 2020. a
Simley, E., Millstein, D., Jeong, S., and Fleming, P.: The value of wake steering wind farm flow control in US energy markets, Wind Energ. Sci., 9, 219–234, https://doi.org/10.5194/wes-9-219-2024, 2024. a
Taschner, E., van Vondelen, A., Verzijlbergh, R., and van Wingerden, J. W.: On the performance of the helix wind farm control approach in the conventionally neutral atmospheric boundary layer, J. Phys. Conf. Ser., 2505, 012006, https://doi.org/10.1088/1742-6596/2505/1/012006, 2023. a
Taschner, E., Becker, M., Verzijlbergh, R., and Van Wingerden, J. W.: Comparison of helix and wake steering control for varying turbine spacing and wind direction, J. Phys. Conf. Ser., 2767, 032023, https://doi.org/10.1088/1742-6596/2767/3/032023, 2024. a, b, c
Türk, M. and Emeis, S.: The dependence of offshore turbulence intensity on wind speed, J. Wind Eng. Ind. Aerod., 98, 466–471, https://doi.org/10.1016/j.jweia.2010.02.005, 2010. a
van Beek, M. T., Viré, A., and Andersen, S. J.: Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm, Energies, 14, https://doi.org/10.3390/en14051293, 2021. a
van der Hoek, D., Doekemeijer, B., Andersson, L. E., and van Wingerden, J. W.: Predicting the benefit of wake steering on the annual energy production of a wind farm using large eddy simulations and Gaussian process regression, J. Phys. Conf. Ser., 1618, 022024, https://doi.org/10.1088/1742-6596/1618/2/022024, 2020. a, b
van der Hoek, D., den Abbeele, B. V., Simao Ferreira, C., and van Wingerden, J. W.: Maximizing wind farm power output with the helix approach: Experimental validation and wake analysis using tomographic particle image velocimetry, Wind Energy, 27, 463–482, https://doi.org/10.1002/we.2896, 2024. a
van Vondelen, A. A., Navalkar, S. T., Kerssemakers, D. R., and van Wingerden, J. W.: Enhanced wake mixing in wind farms using the Helix approach: A loads sensitivity study, in: 2023 American Control Conference (ACC), 831–836, https://doi.org/10.23919/ACC55779.2023.10155965, 2023. a, b
van Vondelen, A. A. W., Coquelet, M., Navalkar, S. T., and van Wingerden, J.-W.: Synchronized Helix wake mixing control, Wind Energ. Sci., 10, 2411–2433, https://doi.org/10.5194/wes-10-2411-2025, 2025. a
van Wingerden, J. W., Fleming, P. A., Göçmen, T., Eguinoa, I., Doekemeijer, B. M., Dykes, K., Lawson, M., Simley, E., King, J., Astrain, D., Iribas, M., Bottasso, C. L., Meyers, J., Raach, S., Kölle, K., and Giebel, G.: Expert Elicitation on Wind Farm Control, J. Phys. Conf. Ser., 1618, 022025, https://doi.org/10.1088/1742-6596/1618/2/022025, 2020. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nature Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Vollmer, L., Steinfeld, G., Heinemann, D., and Kühn, M.: Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study, Wind Energ. Sci., 1, 129–141, https://doi.org/10.5194/wes-1-129-2016, 2016. a
von Brandis, A., Centurelli, G., Schmidt, J., Vollmer, L., Djath, B., and Dörenkämper, M.: An investigation of spatial wind direction variability and its consideration in engineering models, Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, 2023. a
Zahle, F., Barlas, T., Lønbæk, K., Bortolotti, P., Zalkind, D., Wang, L., Labuschagne, C., Sethuraman, L., and Barter, G.: Definition of the IEA Wind 22-Megawatt Offshore Reference Wind Turbine, Tech. Rep. DTU Wind Energy Report E-0243, Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark, ISBN 978-87-87335-71-3, https://doi.org/10.11581/DTU.00000317, 2024. a
Short summary
Wind farm flow control mitigates wake effects within a wind farm by adjusting the turbine settings to improve the overall farm performance. This study quantifies the value of a combined strategy in which each turbine can apply wake steering or the helix wake mixing method. The proposed method is simulated for a large-scale wind farm, for which such a combined strategy provides higher gains in annual energy production compared to the individual techniques.
Wind farm flow control mitigates wake effects within a wind farm by adjusting the turbine...
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