Articles | Volume 10, issue 10
https://doi.org/10.5194/wes-10-2257-2025
© Author(s) 2025. 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-10-2257-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Oct 2025
Research article |
| 21 Oct 2025
| 21 Oct 2025
Dynamic induction control for mitigation of wake-induced power losses: a wind tunnel study under different inflow conditions
Manuel Alejandro Zúñiga Inestroza
CORRESPONDING AUTHOR
Carl von Ossietzky Universität Oldenburg, School of Mathematics and Science, Institute of Physics, 26129 Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
Paul Hulsman
Carl von Ossietzky Universität Oldenburg, School of Mathematics and Science, Institute of Physics, 26129 Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
Vlaho Petrović
Carl von Ossietzky Universität Oldenburg, School of Mathematics and Science, Institute of Physics, 26129 Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
Martin Kühn
Carl von Ossietzky Universität Oldenburg, School of Mathematics and Science, Institute of Physics, 26129 Oldenburg, Germany
ForWind – Center for Wind Energy Research, Küpkersweg 70, 26129 Oldenburg, Germany
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-50, https://doi.org/10.5194/wes-2026-50, 2026
Preprint under review for WES
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Offshore wind farms need accurate wind data, but collecting measurements at sea is often difficult and expensive. In this study, we investigate how wind measurements from a lidar mounted on a moving ship can improve the accuracy of commonly used reanalysis datasets. Using a statistical correction method, we are able to reduce errors in wind speed estimates. This efficient approach can make offshore wind planning and resource assessment more reliable in areas where measurements are limited.
Johannes Paulsen, Gerald Steinfeld, Martin Kühn, and Martin Dörenkämper
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-23, https://doi.org/10.5194/wes-2026-23, 2026
Preprint under review for WES
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We investigated the potential benefits in wake development and power production of a new turbine concept with a low specific rating and low induction and compared it to a reference design with same rated power. Our results show that the design can strongly increase the AEP and shows especially strong benefits during doldrum events and in strongly cluster wake affected areas. While the inner farm wake wakes are strongly decreased by the new concept, the outer farm wakes are slightly stronger.
Daniel Ribnitzky, Vlaho Petrović, and Martin Kühn
Wind Energ. Sci., 11, 469–491, https://doi.org/10.5194/wes-11-469-2026, https://doi.org/10.5194/wes-11-469-2026, 2026
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We developed controllers for the Hybrid-Lambda Rotor, which enables two operating modes below rated power via different tip speed ratios, balancing load reduction and power output. A baseline controller with a model-based wind speed estimator, a load feedback controller and an inflow feed-forward controller were implemented on the MoWiTO 1.8 model turbine and tested in wind tunnel experiments. In depth scaling, considerations ensure the transferability of the results to the full-scale model.
Johannes Paulsen, Jörge Schneemann, Gerald Steinfeld, Frauke Theuer, and Martin Kühn
Wind Energ. Sci., 11, 321–346, https://doi.org/10.5194/wes-11-321-2026, https://doi.org/10.5194/wes-11-321-2026, 2026
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While low-level jets (LLJs) have been well characterised, their impact on offshore wind farms is not well understood. This study uses multi-elevation lidar scans to derive vertical wind profiles up to 350 m, detecting LLJs in up to 22.6 % of available measurements. Further, we analyse their effect on power conversion efficiency using operational wind farm data, observing a slightly negative influence and increased power fluctuations during LLJ events.
Astrid Lampert, Beatriz Cañadillas, Thomas Rausch, Lea Schmitt, Bughsin' Djath, Johannes Schulz-Stellenfleth, Andreas Platis, Kjell zum Berge, Ines Schäfer, Jens Bange, Thomas Neumann, Martin Dörenkämper, Bernhard Stoevesandt, Julia Gottschall, Lukas Vollmer, Stefan Emeis, Mares Barekzai, Simon Siedersleben, Martin Kühn, Gerald Steinfeld, Detlev Heinemann, Joachim Peinke, Hendrik Heißelmann, Jörge Schneemann, Gabriele Centurelli, Philipp Waldmann, and Konrad Bärfuss
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-277, https://doi.org/10.5194/wes-2025-277, 2026
Preprint under review for WES
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David Onnen, Gunner Christian Larsen, Alan Wai Hou Lio, Paul Hulsman, Martin Kühn, and Vlaho Petrović
Wind Energ. Sci., 11, 175–193, https://doi.org/10.5194/wes-11-175-2026, https://doi.org/10.5194/wes-11-175-2026, 2026
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Hugo Rubio, Daniel Hatfield, Charlotte Bay Hasager, Martin Kühn, and Julia Gottschall
Atmos. Meas. Tech., 18, 4949–4968, https://doi.org/10.5194/amt-18-4949-2025, https://doi.org/10.5194/amt-18-4949-2025, 2025
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Wind Energ. Sci., 10, 1849–1867, https://doi.org/10.5194/wes-10-1849-2025, https://doi.org/10.5194/wes-10-1849-2025, 2025
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Daniel Ribnitzky, Vlaho Petrović, and Martin Kühn
Wind Energ. Sci., 10, 1329–1349, https://doi.org/10.5194/wes-10-1329-2025, https://doi.org/10.5194/wes-10-1329-2025, 2025
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In this paper, the Hybrid-Lambda Rotor is scaled to wind tunnel size and validated in wind tunnel experiments. The objectives are to derive a scaling methodology, to investigate the influence of the steep gradients of axial induction along the blade span, and to characterize the near wake. The study reveals complex three-dimensional flow patterns for blade designs with non-uniform loading, and it can offer new inspirations when solving other scaling problems for complex wind turbine systems.
Frauke Theuer, Janna Kristina Seifert, Jörge Schneemann, and Martin Kühn
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Preprint under review for WES
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Anantha Padmanabhan Kidambi Sekar, Paul Hulsman, Marijn Floris van Dooren, and Martin Kühn
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We present induction zone measurements conducted with two synchronised lidars at a two-turbine wind farm. The induction zone flow was characterised for free, fully waked and partially waked flows. Due to the short turbine spacing, the lidars captured the interaction of the atmospheric boundary layer, induction zone and wake, evidenced by induction asymmetry and induction zone–wake interactions. The measurements will aid the process of further improving existing inflow and wake models.
Daniel Ribnitzky, Frederik Berger, Vlaho Petrović, and Martin Kühn
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Balthazar Arnoldus Maria Sengers, Andreas Rott, Eric Simley, Michael Sinner, Gerald Steinfeld, and Martin Kühn
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Paul Hulsman, Luis A. Martínez-Tossas, Nicholas Hamilton, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-112, https://doi.org/10.5194/wes-2023-112, 2023
Manuscript not accepted for further review
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Balthazar Arnoldus Maria Sengers, Gerald Steinfeld, Paul Hulsman, and Martin Kühn
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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 input data, it gives good results.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
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Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, https://doi.org/10.5194/wes-7-2099-2022, 2022
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Remote-sensing-based approaches have shown potential for minute-scale forecasting and need to be further developed towards an operational use. In this work we extend a lidar-based forecast to an observer-based probabilistic power forecast by combining it with a SCADA-based method. We further aggregate individual turbine power using a copula approach. We found that the observer-based forecast benefits from combining lidar and SCADA data and can outperform persistence for unstable stratification.
Frederik Berger, Lars Neuhaus, David Onnen, Michael Hölling, Gerard Schepers, and Martin Kühn
Wind Energ. Sci., 7, 1827–1846, https://doi.org/10.5194/wes-7-1827-2022, https://doi.org/10.5194/wes-7-1827-2022, 2022
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We proof the dynamic inflow effect due to gusts in wind tunnel experiments with MoWiTO 1.8 in the large wind tunnel of ForWind – University of Oldenburg, where we created coherent gusts with an active grid. The effect is isolated in loads and rotor flow by comparison of a quasi-steady and a dynamic case. The observed effect is not caught by common dynamic inflow engineering models. An improvement to the Øye dynamic inflow model is proposed, matching experiment and corresponding FVWM simulations.
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.
Balthazar Arnoldus Maria Sengers, Matthias Zech, Pim Jacobs, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 7, 1455–1470, https://doi.org/10.5194/wes-7-1455-2022, https://doi.org/10.5194/wes-7-1455-2022, 2022
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Wake steering aims to redirect the wake away from a downstream turbine. This study explores the potential of a data-driven surrogate model whose equations can be interpreted physically. It estimates wake characteristics from measurable input variables by utilizing a simple linear model. The model shows encouraging results in estimating available power in the far wake, with significant improvements over currently used analytical models in conditions where wake steering is deemed most effective.
Marijn Floris van Dooren, Anantha Padmanabhan Kidambi Sekar, Lars Neuhaus, Torben Mikkelsen, Michael Hölling, and Martin Kühn
Atmos. Meas. Tech., 15, 1355–1372, https://doi.org/10.5194/amt-15-1355-2022, https://doi.org/10.5194/amt-15-1355-2022, 2022
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The remote sensing technique lidar is widely used for wind speed measurements for both industrial and academic applications. Lidars can measure wind statistics accurately but cannot fully capture turbulent fluctuations in the high-frequency range, since they are partly filtered out. This paper therefore investigates the turbulence spectrum measured by a continuous-wave lidar and analytically models the lidar's measured spectrum with a Lorentzian filter function and a white noise term.
Andreas Rott, Jörge Schneemann, Frauke Theuer, Juan José Trujillo Quintero, and Martin Kühn
Wind Energ. Sci., 7, 283–297, https://doi.org/10.5194/wes-7-283-2022, https://doi.org/10.5194/wes-7-283-2022, 2022
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We present three methods that can determine the alignment of a lidar placed on the transition piece of an offshore wind turbine based on measurements with the instrument: a practical implementation of hard targeting for north alignment, a method called sea surface levelling to determine the levelling of the system from water surface measurements, and a model that can determine the dynamic levelling based on the operating status of the wind turbine.
Paul Hulsman, Martin Wosnik, Vlaho Petrović, Michael Hölling, and Martin Kühn
Wind Energ. Sci., 7, 237–257, https://doi.org/10.5194/wes-7-237-2022, https://doi.org/10.5194/wes-7-237-2022, 2022
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Due to the possibility of mapping the wake fast at multiple locations with the WindScanner, a thorough understanding of the development of the wake is acquired at different inflow conditions and operational conditions. The lidar velocity data and the energy dissipation rate compared favourably with hot-wire data from previous experiments, lending credibility to the measurement technique and methodology used here. This will aid the process to further improve existing wake models.
Frederik Berger, David Onnen, Gerard Schepers, and Martin Kühn
Wind Energ. Sci., 6, 1341–1361, https://doi.org/10.5194/wes-6-1341-2021, https://doi.org/10.5194/wes-6-1341-2021, 2021
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Dynamic inflow denotes the unsteady aerodynamic response to fast changes in rotor loading and leads to load overshoots. We performed a pitch step experiment with MoWiTO 1.8 in the large wind tunnel of ForWind – University of Oldenburg. We measured axial and tangential inductions with a recent method with a 2D-LDA system and performed load and wake measurements. These radius-resolved measurements allow for new insights into the dynamic inflow phenomenon.
Janna Kristina Seifert, Martin Kraft, Martin Kühn, and Laura J. Lukassen
Wind Energ. Sci., 6, 997–1014, https://doi.org/10.5194/wes-6-997-2021, https://doi.org/10.5194/wes-6-997-2021, 2021
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Fluctuations in the power output of wind turbines are one of the major challenges in the integration and utilisation of wind energy. By analysing the power output fluctuations of wind turbine pairs in an offshore wind farm, we show that their correlation depends on their location within the wind farm and their inflow. The main outcome is that these correlation dependencies can be characterised by statistics of the power output of the wind turbines and sorted by a clustering algorithm.
Jörge Schneemann, Frauke Theuer, Andreas Rott, Martin Dörenkämper, and Martin Kühn
Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, https://doi.org/10.5194/wes-6-521-2021, 2021
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A wind farm can reduce the wind speed in front of it just by its presence and thus also slightly impact the available power. In our study we investigate this so-called global-blockage effect, measuring the inflow of a large offshore wind farm with a laser-based remote sensing method up to several kilometres in front of the farm. Our results show global blockage under a certain atmospheric condition and operational state of the wind farm; during other conditions it is not visible in our data.
Cited articles
Barthelmie, R. J., Hansen, K., Frandsen, S. T., Rathmann, O., Schepers, J. G., Schlez, W., Phillips, J., Rados, K., Zervos, A., Politis, E. S., and Chaviaropoulos, P. K.: Modelling and measuring flow and wind turbine wakes in large wind farms offshore, Wind Energy, 12, 431–444, https://doi.org/10.1002/we.348, 2009. a
Bossanyi, E. A.: The Design of closed loop controllers for wind turbines, Wind Energy, 3, 149–163, https://doi.org/10.1002/we.34, 2000. 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
Cal, R. B., Lebrón, J., Castillo, L., Kang, H. S., and Meneveau, C.: Experimental study of the horizontally averaged flow structure in a model wind-turbine array boundary layer, Journal of Renewable and Sustainable Energy, 2, 013106, https://doi.org/10.1063/1.3289735, 2010. a
Chamorro, L. P., Arndt, R., and Sotiropoulos, F.: Reynolds number dependence of turbulence statistics in the wake of wind turbines, Wind Energy, 15, 733–742, https://doi.org/10.1002/we.501, 2012. a
Coquelet, M., Moens, M., Bricteux, L., Crismer, J.-B., and Chatelain, P.: Performance assessment of wake mitigation strategies, J. Phys. Conf. Ser., 2265, 032078, https://doi.org/10.1088/1742-6596/2265/3/032078, 2022. a
Dallas, S., Stock, A., and Hart, E.: Control-oriented modelling of wind direction variability, Wind Energy Science, 9, 841–867, https://doi.org/10.5194/wes-9-841-2024, 2024. a
Fleming, P., Gebraad, P. M., Lee, S., van Wingerden, J.-W., Johnson, K., Churchfield, M., Michalakes, J., Spalart, P., and Moriarty, P.: Simulation comparison of wake mitigation control strategies for a two-turbine case, Wind Energy, 18, 2135–2143, https://doi.org/10.1002/we.1810, 2015. a
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, Renewable Energy, 70, 211–218, https://doi.org/10.1016/j.renene.2014.02.015, 2014. a
Frederik, J. A. and van Wingerden, J.-W.: On the load impact of dynamic wind farm wake mixing strategies, Renewable Energy, 194, 582–595, https://doi.org/10.1016/j.renene.2022.05.110, 2022. a
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, 2020a. a
Frederik, J. A., Weber, R., Cacciola, S., Campagnolo, F., Croce, A., Bottasso, C., and van Wingerden, J.-W.: Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments, Wind Energ. Sci., 5, 245–257, https://doi.org/10.5194/wes-5-245-2020, 2020b. a, b, c, d
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
Goit, J. P. and Meyers, J.: Optimal control of energy extraction in wind-farm boundary layers, J. Fluid Mech., 768, 5–50, https://doi.org/10.1017/jfm.2015.70, 2015. a
Goit, J. P., Munters, W., and Meyers, J.: Optimal Coordinated Control of Power Extraction in LES of a Wind Farm with Entrance Effects, Energies, 9, https://doi.org/10.3390/en9010029, 2016. a
Houck, D. R.: Review of wake management techniques for wind turbines, Wind Energy, 25, 195–220, https://doi.org/10.1002/we.2668, 2022. a, b, c
Howland, M. F., González, C. M., Martínez, J. J. P., Quesada, J. B., Larrañaga, F. P., Yadav, N. K., Chawla, J. S., and Dabiri, J. O.: Influence of atmospheric conditions on the power production of utility-scale wind turbines in yaw misalignment, Journal of Renewable and Sustainable Energy, 12, 063307, https://doi.org/10.1063/5.0023746, 2020. a
Hulsman, P., Wosnik, M., Petrović, V., Hölling, M., and Kühn, M.: Turbine Wake Deflection Measurement in a Wind Tunnel with a Lidar WindScanner, Journal of Physics: Conference Series, 1452, 012007, https://doi.org/10.1088/1742-6596/1452/1/012007, 2020. a
Hulsman, P., Sucameli, C., Petrović, V., Rott, A., Gerds, A., and Kühn, M.: Turbine power loss during yaw-misaligned free field tests at different atmospheric conditions, Journal of Physics: Conference Series, 2265, 032074, https://doi.org/10.1088/1742-6596/2265/3/032074, 2022a. a
Hulsman, P., Howland, M., Göçmen, T., Petrović, V., and Kühn, M.: Self-Learning Data-Driven Wind Farm Control Strategy Using Field Measurements, 1057–1064, https://doi.org/10.23919/ACC60939.2024.10644839, 2024. a
Jonkman, B. J.: Turbsim User's Guide: Version 1.50, https://doi.org/10.2172/965520, 2009.
Jonkman, J., Butterfield, S., Musial,W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, Technical Report NREL/TP-500-38060, https://doi.org/10.2172/947422, 2009b a
Kheirabadi, A. C. and Nagamune, R.: A quantitative review of wind farm control with the objective of wind farm power maximization, J. Wind Eng. Ind. Aerodyn., 192, 45–73, https://doi.org/10.1016/j.jweia.2019.06.015, 2019. a
Kröger, L., Frederik, J., van Wingerden, J.-W., Peinke, J., and Hölling, M.: Generation of user defined turbulent inflow conditions by an active grid for validation experiments, 1037, 052002, https://doi.org/10.1088/1742-6596/1037/5/052002, 2018. a
Lignarolo, L. E. M., Ragni, D., Scarano, F., Simão Ferreira, C., and van Bussel, G. J. W.: Tip-vortex instability and turbulent mixing in wind-turbine wakes, Journal of Fluid Mechanics, 781, 467–493, https://doi.org/10.1017/jfm.2015.470, 2015. a, b
Meyers, J., Bottasso, C., Dykes, K., Fleming, P., Gebraad, P., Giebel, G., Göçmen, T., and van Wingerden, J.-W.: Wind farm flow control: prospects and challenges, Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, 2022. a, b
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, b, c, d
Neuhaus, L., Berger, F., Peinke, J., and Hölling, M.: Exploring the capabilities of active grids, Exp. Fluids, 62, 130, https://doi.org/10.1007/s00348-021-03224-5, 2021. a
Neunaber, I., Hölling, M., Stevens, R. J. A. M., Schepers, G., and Peinke, J.: Distinct Turbulent Regions in the Wake of a Wind Turbine and Their Inflow-Dependent Locations: The Creation of a Wake Map, Energies, 13, https://doi.org/10.3390/en13205392, 2020. a
National Renewable Energy Laboratory: TurbSim v1.50, GitHub [code], https://github.com/old-NWTC/TurbSim (last access: 15 April 2025), 2009. a
National Renewable Energy Laboratory: OpenFAST v3.4.1, GitHub [code], https://github.com/OpenFAST/openfast (last access: 15 April 2025), 2023. a
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-turbine and wind-farm flows: A review, Boundary Layer Meteorol., 174, 1–59, https://doi.org/10.1007/s10546-019-00473-0, 2020. a, b, c
Schottler, J., Hölling, A., Peinke, J., and Hölling, M.: Design and implementation of a controllable model wind turbine for experimental studies, J. Phys. Conf. Ser., 753, 072030, https://doi.org/10.1088/1742-6596/753/7/072030, 2016. a
Taschner, E., van Vondelen, A., Verzijlbergh, R., and van Wingerden, J.: On the performance of the helix wind farm control approach in the conventionally neutral atmospheric boundary layer, Journal of Physics: Conference Series, 2505, 012006, https://doi.org/10.1088/1742-6596/2505/1/012006, 2023. a
Uluocak, S., Theuer, F., Neuhaus, L., Inestroza, M. A. Z., Hulsman, P., and Kühn, M.: The lidar probe volume averaging effect: A wind tunnel investigation in streamwise turbulence with continuous-wave lidar, Journal of Physics: Conference Series, 2767, 042027, https://doi.org/10.1088/1742-6596/2767/4/042027, 2024. a
van der Hoek, D., Kanev, S., Allin, J., Bieniek, D., and Mittelmeier, N.: Effects of axial induction control on wind farm energy production – A field test, Renewable Energy, 140, 994–1003, https://doi.org/10.1016/j.renene.2019.03.117, 2019. a
van der Hoek, D., Frederik, J., Huang, M., Scarano, F., Simao Ferreira, C., and van Wingerden, J.-W.: Experimental analysis of the effect of dynamic induction control on a wind turbine wake, Wind Energ. Sci., 7, 1305–1320, https://doi.org/10.5194/wes-7-1305-2022, 2022. a, b, c
van der Hoek, D., Ferreira, C. S., and Wingerden, J.-W. V.: Experimental comparison of induction control methods for wind farm power maximization on a scaled two-turbine setup, Journal of Physics: Conference Series, 2767, 092064, https://doi.org/10.1088/1742-6596/2767/9/092064, 2024. a, b
van Dooren, M. F., Campagnolo, F., Sjöholm, M., Angelou, N., Mikkelsen, T., and Kühn, M.: Demonstration and uncertainty analysis of synchronised scanning lidar measurements of 2-D velocity fields in a boundary-layer wind tunnel, Wind Energ. Sci., 2, 329–341, https://doi.org/10.5194/wes-2-329-2017, 2017. a, b, c, d
van Dooren, M. F., Kidambi Sekar, A. P., Neuhaus, L., Mikkelsen, T., Hölling, M., and Kühn, M.: Modelling the spectral shape of continuous-wave lidar measurements in a turbulent wind tunnel, Atmos. Meas. Tech., 15, 1355–1372, https://doi.org/10.5194/amt-15-1355-2022, 2022. 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
Wagner, R., Courtney, M., Gottschall, J., and Lindelöw-Marsden, P.: Accounting for the speed shear in wind turbine power performance measurement, Wind Energy, 14, 993–1004, https://doi.org/10.1002/we.509, 2011. a
Wang, C., Campagnolo, F., Sharma, A., and Bottasso, C. L.: Effects of dynamic induction control on power and loads, by LES-ALM simulations and wind tunnel experiments, J. Phys. Conf. Ser., 1618, 022036, https://doi.org/10.1088/1742-6596/1618/2/022036, 2020. a, b, c
Wang, C., Campagnolo, F., Canet, H., Barreiro, D. J., and Bottasso, C. L.: How realistic are the wakes of scaled wind turbine models?, Wind Energ. Sci., 6, 961–981, https://doi.org/10.5194/wes-6-961-2021, 2021. a
Zúñiga Inestroza, M. A., Hulsman, P., and Petrović, V.: On the impact of different static induction control strategies on a wind turbine wake, J. Phys. Conf. Ser., 2767, 092082, https://doi.org/10.1088/1742-6596/2767/9/092082, 2024. a, b
Zúñiga Inestroza, M. A., Hulsman, P., Petrović, V., and Kühn, M.: Data Supplement for “Dynamic induction control for mitigation of wake-induced power losses: a wind tunnel study under different inflow conditions”, Zenodo [data set], https://doi.org/10.5281/zenodo.15356141, 2025. a
Short summary
Wake effects cause power losses that degrade wind farm efficiency. This paper presents a wind tunnel investigation of dynamic induction control (DIC), a strategy to mitigate wake losses by improving turbine–flow interactions. WindScanner lidar measurements are used to explore the wake development of model turbines in response to DIC. Our results demonstrate consistent benefits and adaptability under realistic inflow conditions, highlighting DIC’s potential to increase wind farm power production.
Wake effects cause power losses that degrade wind farm efficiency. This paper presents a wind...
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