Articles | Volume 10, issue 5
https://doi.org/10.5194/wes-10-987-2025
© 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-10-987-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Analysis and calibration of optimal power balance rotor-effective wind speed estimation schemes for large-scale wind turbines
Atindriyo Kusumo Pamososuryo
CORRESPONDING AUTHOR
Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, the Netherlands
Fabio Spagnolo
Vestas Wind Systems A/S, Hedeager 42, 8200 Aarhus N, Denmark
Sebastiaan Paul Mulders
Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, the Netherlands
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Jesse Ishi Storm Hummel, Jens Kober, and Sebastiaan Paul Mulders
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-153, https://doi.org/10.5194/wes-2024-153, 2025
Revised manuscript accepted for WES
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Wind turbines have grown dramatically over the last decades. Since wind speed increases with height, each blade experiences high wind speed when pointing up and less wind when pointing down, causing oscillations. These oscillations can be reduced with individual pitch control (IPC), at the expense of constant blade actuation, hindering industry adoption. This work proposes two output-constrained IPC methods to facilitate the trade-off between load reduction and actuation increase.
Guido Lazzerini, Jacob Deleuran Grunnet, Tobias Gybel Hovgaard, Fabio Caponetti, Vasu Datta Madireddi, Delphine De Tavernier, and Sebastiaan Paul Mulders
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-151, https://doi.org/10.5194/wes-2024-151, 2024
Revised manuscript accepted for WES
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Large wind turbines face design challenges due to increased flexibility of blades. Conventional control strategies fail under large deformations, impacting performance. We present COFLEX, a feedforward-feedback control scheme, addressing flexibility and overcoming the limitations of conventional strategies. By testing it on the IEA 15 MW turbine with realistic wind conditions, we demonstrated improvements to power by up to 5 %, while constraining blade deflections.
Livia Brandetti, Sebastiaan Paul Mulders, Roberto Merino-Martinez, Simon Watson, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 471–493, https://doi.org/10.5194/wes-9-471-2024, https://doi.org/10.5194/wes-9-471-2024, 2024
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This research presents a multi-objective optimisation approach to balance vertical-axis wind turbine (VAWT) performance and noise, comparing the combined wind speed estimator and tip-speed ratio (WSE–TSR) tracking controller with a baseline. Psychoacoustic annoyance is used as a novel metric for human perception of wind turbine noise. Results showcase the WSE–TSR tracking controller’s potential in trading off the considered objectives, thereby fostering the deployment of VAWTs in urban areas.
Livia Brandetti, Sebastiaan Paul Mulders, Yichao Liu, Simon Watson, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1553–1573, https://doi.org/10.5194/wes-8-1553-2023, https://doi.org/10.5194/wes-8-1553-2023, 2023
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This research presents the additional benefits of applying an advanced combined wind speed estimator and tip-speed ratio tracking (WSE–TSR) controller compared to the baseline Kω2. Using a frequency-domain framework and an optimal calibration procedure, the WSE–TSR tracking control scheme shows a more flexible trade-off between conflicting objectives: power maximisation and load minimisation. Therefore, implementing this controller on large-scale wind turbines will facilitate their operation.
Sebastiaan Paul Mulders, Niels Frederik Boudewijn Diepeveen, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 615–638, https://doi.org/10.5194/wes-3-615-2018, https://doi.org/10.5194/wes-3-615-2018, 2018
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The modeling, operating strategy, and controller design for an actual in-field wind turbine with a fixed-displacement hydraulic drivetrain are presented. An analysis is given on a passive torque control strategy for below-rated operation. The turbine lacks the option to influence the system torque by a generator, so the turbine is regulated by a spear valve in the region between below- and above-rated operation. The control design is evaluated on a real-world 500 kW hydraulic wind turbine.
Edwin van Solingen, Sebastiaan Paul Mulders, and Jan-Willem van Wingerden
Wind Energ. Sci., 2, 153–173, https://doi.org/10.5194/wes-2-153-2017, https://doi.org/10.5194/wes-2-153-2017, 2017
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The aim of this paper is to show that with an automated tuning strategy, wind turbine control performance can be significantly increased. To this end, iterative feedback tuning (IFT) is applied to two different turbine controllers. The results obtained by high-fidelity simulations indicate significant performance improvements over baseline controllers. It is concluded that IFT of turbine controllers has the potential to become a valuable tool for improving wind turbine performance.
Related subject area
Thematic area: Dynamics and control | Topic: Dynamics and aeroservoelasticity
Coleman-free aero-elastic stability methods for three- and two-bladed floating wind turbines
Investigating the interactions between wakes and floating wind turbines using FAST.Farm
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Bogdan Pamfil, Henrik Bredmose, and Taeseong Kim
Wind Energ. Sci., 10, 827–856, https://doi.org/10.5194/wes-10-827-2025, https://doi.org/10.5194/wes-10-827-2025, 2025
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A floating wind turbine time domain model, which considers dynamic stall, is used to develop Coleman-free aero-elastic stability analysis methods, namely Hill's and Floquet's. We clarify how the floater tilt is involved in the stability analysis, show damping effects of aerodynamic states, prove that results of both methods agree and can reproduce the forward- and backward-whirling rotor modes in a Coleman-based analysis, and demonstrate that both methods can be applied to a two-bladed rotor.
Lucas Carmo, Jason Jonkman, and Regis Thedin
Wind Energ. Sci., 9, 1827–1847, https://doi.org/10.5194/wes-9-1827-2024, https://doi.org/10.5194/wes-9-1827-2024, 2024
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As floating wind turbines progress to arrays with multiple units, it becomes important to understand how the wake of a floating turbine affects the performance of other units in the array. Due to the compliance of the floating substructure, the wake of a floating wind turbine may behave differently from that of a fixed turbine. In this work, we present an investigation of the mutual interaction between the motions of floating wind turbines and wakes.
Hendrik Verdonck, Oliver Hach, Jelmer D. Polman, Otto Schramm, Claudio Balzani, Sarah Müller, and Johannes Rieke
Wind Energ. Sci., 9, 1747–1763, https://doi.org/10.5194/wes-9-1747-2024, https://doi.org/10.5194/wes-9-1747-2024, 2024
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Aeroelastic stability simulations are needed to guarantee the safety and overall robust design of wind turbines. To increase our confidence in these simulations in the future, the sensitivity of the stability analysis with respect to variability in the structural properties of the wind turbine blades is investigated. Multiple state-of-the-art tools are compared and the study shows that even though the tools predict similar stability behavior, the sensitivity might be significantly different.
Marta Bertelè, Paul J. Meyer, Carlo R. Sucameli, Johannes Fricke, Anna Wegner, Julia Gottschall, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1419–1429, https://doi.org/10.5194/wes-9-1419-2024, https://doi.org/10.5194/wes-9-1419-2024, 2024
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A neural observer is used to estimate shear and veer from the operational data of a large wind turbine equipped with blade load sensors. Comparison with independent measurements from a nearby met mast and profiling lidar demonstrate the ability of the
rotor as a sensorconcept to provide high-quality estimates of these inflow quantities based simply on already available standard operational data.
Christian Wiedemann, Hendrik Bette, Matthias Wächter, Jan A. Freund, Thomas Guhr, and Joachim Peinke
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-52, https://doi.org/10.5194/wes-2024-52, 2024
Revised manuscript accepted for WES
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This study utilizes a method to analyze power conversion dynamics across different operational states, addressing non-stationarity with a correlation matrix algorithm. Findings reveal distinct dynamics for each state, emphasizing their impact on system behavior and offering a solution to hysteresis effects in power conversion dynamics.
Kristian Ladefoged Ebbehøj, Philippe Jacques Couturier, Lars Morten Sørensen, and Jon Juel Thomsen
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This paper experimentally validates a novel method for characterizing wind turbine dynamics based on vibration measurements. The dynamics of wind turbines can change over short time periods if the operational conditions change. In such cases, conventional methods are inadequate. The validation is performed with a controlled laboratory experiment and a full-scale wind turbine test. More accurate characterization could lead to more efficient wind turbine designs and in turn cheaper wind energy.
Emmanuel Branlard, Jason Jonkman, Cameron Brown, and Jiatian Zhang
Wind Energ. Sci., 9, 1–24, https://doi.org/10.5194/wes-9-1-2024, https://doi.org/10.5194/wes-9-1-2024, 2024
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In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The article present methods to obtain reduced-order models of floating wind turbines. The models are used to form a digital twin which combines measurements from the TetraSpar prototype (a full-scale floating offshore wind turbine) to estimate signals that are not typically measured.
Jaime Liew, Tuhfe Göçmen, Alan W. H. Lio, and Gunner Chr. Larsen
Wind Energ. Sci., 8, 1387–1402, https://doi.org/10.5194/wes-8-1387-2023, https://doi.org/10.5194/wes-8-1387-2023, 2023
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We present recent research on dynamically modelling wind farm wakes and integrating these enhancements into the wind farm simulator, HAWC2Farm. The simulation methodology is showcased by recreating dynamic scenarios observed in the Lillgrund offshore wind farm. We successfully recreate scenarios with turning winds, turbine shutdown events, and wake deflection events. The research provides opportunities to better identify wake interactions in wind farms, allowing for more reliable designs.
Ásta Hannesdóttir, David R. Verelst, and Albert M. Urbán
Wind Energ. Sci., 8, 231–245, https://doi.org/10.5194/wes-8-231-2023, https://doi.org/10.5194/wes-8-231-2023, 2023
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In this work we use observations of large coherent fluctuations to define a probabilistic gust model. The gust model provides the joint description of the gust rise time, amplitude, and direction change. We perform load simulations with a coherent gust according to the wind turbine safety standard and with the probabilistic gust model. A comparison of the simulated loads shows that the loads from the probabilistic gust model can be significantly higher due to variability in the gust parameters.
Ozan Gözcü, Emre Barlas, and Suguang Dou
Wind Energ. Sci., 8, 109–124, https://doi.org/10.5194/wes-8-109-2023, https://doi.org/10.5194/wes-8-109-2023, 2023
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This study proposes a fast correction method for modal-based reduced-order models to account for geometric nonlinearities linked to large bending deflections in cantilever beam-like engineering structures. The large deflections cause secondary motions such as axial and torsional motions when the structures go through bending deflections. The method relies on pre-computed correction terms and thus adds negligibly small extra computational cost to the time domain analyses of the dynamic response.
Emmanuel Branlard and Jens Geisler
Wind Energ. Sci., 7, 2351–2371, https://doi.org/10.5194/wes-7-2351-2022, https://doi.org/10.5194/wes-7-2351-2022, 2022
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The article presents a framework to obtain the linear and nonlinear equations of motion of a multibody system including rigid and flexible bodies. The method yields compact symbolic equations of motion. The applications are many, such as time-domain simulation, stability analyses, frequency domain analyses, advanced controller design, state observers, and digital twins.
Cited articles
Abbas, N. J., Zalkind, D. S., Pao, L., and Wright, A.: A reference open-source controller for fixed and floating offshore wind turbines, Wind Energ. Sci., 7, 53–73, https://doi.org/10.5194/wes-7-53-2022, 2022. a
Bortolotti, P., Tarres, H. C., Dykes, K., Merz, K., Sethuraman, L., Verelst, D., and Zahle, F.: IEA Wind Task 37 on Systems Engineering in Wind Energy – WP2.1 Reference Wind Turbines, Tech. rep., International Energy Agency, https://www.nrel.gov/docs/fy19osti/73492.pdf (last access: 5 November 2024), 2019. 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, b, c, d
Boukhezzar, B. and Siguerdidjane, H.: Nonlinear Control of a Variable-Speed Wind Turbine Using a Two-Mass Model, IEEE T. Energy Conver., 26, 149–162, https://doi.org/10.1109/TEC.2010.2090155, 2011. a, b, c, d
Brandetti, L., Liu, Y., Mulders, S. P., Ferreira, C., Watson, S., and van Wingerden, J. W.: On the Ill-Conditioning of the Combined Wind Speed Estimator and Tip-Speed Ratio Tracking Control Scheme, J. Phys. Conf. Ser., 2265, 032085, https://doi.org/10.1088/1742-6596/2265/3/032085, 2022. a, b, c, d
Brandetti, L., Mulders, S. P., Liu, Y., Watson, S., and van Wingerden, J.-W.: Analysis and multi-objective optimisation of wind turbine torque control strategies, Wind Energ. Sci., 8, 1553–1573, https://doi.org/10.5194/wes-8-1553-2023, 2023. a, b
Burton, T., Jenkins, N., Sharpe, D., and Bossanyi, E.: Wind Energy Handbook, John Wiley & Sons, Ltd, Chichester, UK, ISBN 978-1-119-99271-4, https://doi.org/10.1002/9781119992714, 2011. a
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G., Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.: Definition of the IEA Wind 15-Megawatt Offshore Reference Wind Turbine Technical Report, Tech. rep., 2020. a
Global Wind Energy Council: Global Wind Report 2024, Report, Global Wind Energy Council, Belgium, https://www.gwec.net/reports/globalwindreport/2024 (last access: 5 November 2024), 2024. a
Hovgaard, T. G., Boyd, S., and Jørgensen, J. B.: Model Predictive Control for Wind Power Gradients, Wind Energy, 18, 991–1006, https://doi.org/10.1002/we.1742, 2015. a
IEEE: IEEE Recommended Practice for Excitation System Models for Power System Stability Studies, IEEE Std 421.5-2016 (Revision of IEEE Std 421.5-2005), 1–207, https://doi.org/10.1109/IEEESTD.2016.7553421, 2016. a
Jonkman, B., Platt, A., Mudafort, R. M., Branlard, E., Sprague, M., Ross, H., jjonkman, HaymanConsulting, Slaughter, D., Hall, M., Vijayakumar, G., Buhl, M., Russell9798, Bortolotti, P., reos rcrozier, Ananthan, S., RyanDavies19, S., M., Rood, J., rdamiani, nrmendoza, sinolonghai, pschuenemann, ashesh2512, kshaler, Housner, S., psakievich, Wang, L., Bendl, K., and Carmo, L.: OpenFAST/openfast: v3.5.3, Zenodo [code], https://doi.org/10.5281/zenodo.10962897, 2024. a
Jonkman, B. J.: TurbSim User's Guide v2.00.00, Renew. Energ., https://www.nrel.gov/docs/libraries/wind-docs/turbsim_v2-00-pdf.pdf?sfvrsn=5a0a30f8_1 (last access: 5 November 2024), 2014. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, Tech. rep., National Renewable Energy Laboratory (NREL), Golden, CO, https://doi.org/10.2172/947422, 2009. a
Knudsen, T. and Bak, T.: Simple Model for Describing and Estimating Wind Turbine Dynamic Inflow, in: 2013 American Control Conference, Washington, DC, USA, 17–19 June 2013, 640–646, https://doi.org/10.1109/ACC.2013.6579909, 2013. a
Koerber, A. and King, R.: Combined Feedback–Feedforward Control of Wind Turbines Using State-Constrained Model Predictive Control, IEEE Transactions on Control Systems Technology, 21, 1117–1128, https://doi.org/10.1109/TCST.2013.2260749, 2013. a, b
Kumar, A. and Stol, K.: Scheduled Model Predictive Control of a Wind Turbine, in: 47th AIAA Aerospace Sciences Meeting Including The New Horizons Forum and Aerospace Exposition, vol. 5, American Institute of Aeronautics and Astronautics, Reston, Virigina, 5–8 January 2009, 3860–3870, ISBN 978-1-60086-973-0, https://doi.org/10.2514/6.2009-481, 2009. a
Lara, M., Vázquez, F., van Wingerden, J. W., Mulders, S. P., and Garrido, J.: Multi-Objective Optimization of Individual Pitch Control for Blade Fatigue Load Reductions for a 15 MW Wind Turbine, in: 2024 European Control Conference (ECC), Stockholm, Sweden, 25–28 June 2024, 669–674, https://doi.org/10.23919/ECC64448.2024.10590830, 2024. a
Lazzerini, G., Deleuran Grunnet, J., Gybel Hovgaard, T., Caponetti, F., Datta Madireddi, V., De Tavernier, D., and Mulders, S. P.: COFLEX: A novel set point optimiser and feedforward-feedback control scheme for large flexible wind turbines, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2024-151, in review, 2024. a, b, c, d
Moustakis, N., Mulders, S. P., Kober, J., and Wingerden, J. W.: A Practical Bayesian Optimization Approach for the Optimal Estimation of the Rotor Effective Wind Speed, Proceedings of the American Control Conference, Philadelphia, PA, USA, 10–12 July 2019, 4179–4185, 2019. a
Mulders, S., Brandetti, L., Spagnolo, F., Liu, Y., Christensen, P., and van Wingerden, J. W.: A Learning Algorithm for the Calibration of Internal Model Uncertainties in Advanced Wind Turbine Controllers: A Wind Speed Measurement-Free Approach, in: 2023 American Control Conference (ACC), IEEE, San Diego, CA, USA, 1486–1492, ISBN 9798350328066, https://doi.org/10.23919/ACC55779.2023.10156125, 2023a. a, b
Mulders, S. P., Pamososuryo, A. K., and van Wingerden, J. W.: Efficient Tuning of Individual Pitch Control: A Bayesian Optimization Machine Learning Approach, J. Phys. Conf. Ser., 1618, 022039, https://doi.org/10.1088/1742-6596/1618/2/022039, 2020. a
Mulders, S. P., Liu, Y., Spagnolo, F., Christensen, P. B., and van Wingerden, J. W.: An Iterative Data-Driven Learning Algorithm for Calibration of the Internal Model in Advanced Wind Turbine Controllers, IFAC PapersOnLine, 56, 8406–8413, https://doi.org/10.1016/j.ifacol.2023.10.1035, 2023b. a
Odgaard, P. F., Hovgaard, T. G., and Wiesniewski, R.: Model Predictive Control for Wind Turbine Power Boosting, 2016 European Control Conference, ECC 2016, Aalborg, Denmark, 29 June–1 July 2016, 1457–1462, https://doi.org/10.1109/ECC.2016.7810495, 2017. a
Ortega, R., Mancilla-David, F., and Jaramillo, F.: A Globally Convergent Wind Speed Estimator for Wind Turbine Systems, Int. J. Adapt. Control, 27, 413–425, https://doi.org/10.1002/acs.2319, 2013. a, b, c, d
Pamososuryo, A. K., Liu, Y., Gybel Hovgaard, T., Ferrari, R., and van Wingerden, J. W.: Convex Economic Model Predictive Control for Blade Loads Mitigation on Wind Turbines, Wind Energy, 26, 1276–1298, https://doi.org/10.1002/we.2869, 2023. a
Pamososuryo, A. K., Spagnolo, F., and Mulders, S. P.: Analysis and calibration of optimal power balance rotor-effective wind speed estimation schemes for large-scale wind turbines – Code and Data, Zenodo [code] and [data set], https://doi.org/10.5281/zenodo.15491426, 2024. a, b, c
Rodriguez, A. G. G., Rodriguez, A. G. G., and Payán, M. B.: Estimating wind turbines mechanical constants, Renewable Energy & Power Quality Journal, 1, 697–704, https://doi.org/10.24084/repqj05.361, 2007. a
Soltani, M. N., Knudsen, T., Svenstrup, M., Wisniewski, R., Brath, P., Ortega, R., and Johnson, K. E.: Estimation of Rotor Effective Wind Speed: A Comparison, IEEE T. Contr. Syst. T., 21, 1155–1167, https://doi.org/10.1109/TCST.2013.2260751, 2013. a, b, c, d
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O., Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki, V., Lundquist, J. K., Manwell, J., Marquis, M., Meneveau, C., Moriarty, P., Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J., Robertson, A., Sanz Rodrigo, J., Sempreviva, A. M., Smith, J. C., Tuohy, A., and Wiser, R.: Grand Challenges in the Science of Wind Energy, Science, 366, eaau2027, https://doi.org/10.1126/science.aau2027, 2019. a
Verhaegen, M. and Verdult, V.: Filtering and System Identification: A Least Squares Approach, Cambridge University Press, Cambridge, Online ISBN 9780511618888, https://doi.org/10.1017/CBO9780511618888, 2007. a, b
Zahle, F., Barlas, A., Loenbaek, 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., Technical University of Denmark, https://doi.org/10.11581/DTU.00000317, 2024. a
Short summary
As wind turbines grow in size, measuring wind speed accurately becomes challenging, impacting their performance. Traditional sensors cannot capture wind variations across large rotor areas. To address this, a new method is developed to estimate wind speed accurately, accounting for these variations. Using mid-fidelity simulations, our approach showed better tracking, better noise resilience, and easy tuning for different turbine sizes.
As wind turbines grow in size, measuring wind speed accurately becomes challenging, impacting...
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