Articles | Volume 9, issue 2
https://doi.org/10.5194/wes-9-471-2024
© Author(s) 2024. 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-9-471-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2024
Research article |
| 01 Mar 2024
| 01 Mar 2024
Multi-objective calibration of vertical-axis wind turbine controllers: balancing aero-servo-elastic performance and noise
Flow Physics and Technology, Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Delft Center for Systems and Control, Faculty of Mechanical Engineering, Delft University of Technology, Delft, the Netherlands
Sebastiaan Paul Mulders
CORRESPONDING AUTHOR
Delft Center for Systems and Control, Faculty of Mechanical Engineering, Delft University of Technology, Delft, the Netherlands
Roberto Merino-Martinez
Aircraft Noise and Climate Effects, Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Simon Watson
Flow Physics and Technology, Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Jan-Willem van Wingerden
Delft Center for Systems and Control, Faculty of Mechanical Engineering, Delft University of Technology, Delft, the Netherlands
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Sarah J. Ollier and Simon J. Watson
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Daniel van den Berg, Delphine de Tavernier, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 849–864, https://doi.org/10.5194/wes-8-849-2023, https://doi.org/10.5194/wes-8-849-2023, 2023
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Wind turbines placed in farms interact with their wake, lowering the power production of the wind farm. This can be mitigated using so-called wake mixing techniques. This work investigates the coupling between the pulse wake mixing technique and the motion of floating wind turbines using the pulse. Frequency response experiments and time domain simulations show that extra movement is undesired and that the
optimalexcitation frequency is heavily platform dependent.
Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Göçmen, and Jan-Willem van Wingerden
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Marcus Becker, Bastian Ritter, Bart Doekemeijer, Daan van der Hoek, Ulrich Konigorski, Dries Allaerts, and Jan-Willem van Wingerden
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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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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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Amir R. Nejad, Jonathan Keller, Yi Guo, Shawn Sheng, Henk Polinder, Simon Watson, Jianning Dong, Zian Qin, Amir Ebrahimi, Ralf Schelenz, Francisco Gutiérrez Guzmán, Daniel Cornel, Reza Golafshan, Georg Jacobs, Bart Blockmans, Jelle Bosmans, Bert Pluymers, James Carroll, Sofia Koukoura, Edward Hart, Alasdair McDonald, Anand Natarajan, Jone Torsvik, Farid K. Moghadam, Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, https://doi.org/10.5194/wes-7-387-2022, 2022
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This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the energy conversion systems transferring the kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps.
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.
Alessandro Fontanella, Mees Al, Jan-Willem van Wingerden, and Marco Belloli
Wind Energ. Sci., 6, 885–901, https://doi.org/10.5194/wes-6-885-2021, https://doi.org/10.5194/wes-6-885-2021, 2021
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Floating wind is a key technology to harvest the abundant wind energy resource of deep waters. This research introduces a new way of controlling the wind turbine to better deal with the action of waves. The turbine is made aware of the incoming waves, and the information is exploited to enhance power production.
Bart M. Doekemeijer, Stefan Kern, Sivateja Maturu, Stoyan Kanev, Bastian Salbert, Johannes Schreiber, Filippo Campagnolo, Carlo L. Bottasso, Simone Schuler, Friedrich Wilts, Thomas Neumann, Giancarlo Potenza, Fabio Calabretta, Federico Fioretti, and Jan-Willem van Wingerden
Wind Energ. Sci., 6, 159–176, https://doi.org/10.5194/wes-6-159-2021, https://doi.org/10.5194/wes-6-159-2021, 2021
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This article presents the results of a field experiment investigating wake steering on an onshore wind farm. The measurements show that wake steering leads to increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions. The results suggest that further research is necessary before wake steering will consistently lead to energy gains in wind farms.
Bedassa R. Cheneka, Simon J. Watson, and Sukanta Basu
Wind Energ. Sci., 5, 1731–1741, https://doi.org/10.5194/wes-5-1731-2020, https://doi.org/10.5194/wes-5-1731-2020, 2020
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Wind power ramps have important characteristics for the planning and integration of wind power production into electricity. We present a new and simple algorithm that detects wind power ramp characteristics. The algorithm classifies wind power production into ramp-ups, ramp-downs, and no-ramps; and it can detect wind power ramp characteristics that show a temporal increasing (decreasing) power capacity.
Mark Schelbergen, Peter C. Kalverla, Roland Schmehl, and Simon J. Watson
Wind Energ. Sci., 5, 1097–1120, https://doi.org/10.5194/wes-5-1097-2020, https://doi.org/10.5194/wes-5-1097-2020, 2020
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We have presented a methodology for including multiple wind profile shapes in a wind resource description that are identified using a data-driven approach. These shapes go beyond the height range for which conventional wind profile relationships are developed. Moreover, they include non-monotonic shapes such as low-level jets. We demonstrated this methodology for an on- and offshore reference location using DOWA data and efficiently estimated the annual energy production of a pumping AWE system.
Joeri Alexis Frederik, Robin Weber, Stefano Cacciola, Filippo Campagnolo, Alessandro Croce, Carlo Bottasso, and Jan-Willem van Wingerden
Wind Energ. Sci., 5, 245–257, https://doi.org/10.5194/wes-5-245-2020, https://doi.org/10.5194/wes-5-245-2020, 2020
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The interaction between wind turbines in a wind farm through their wakes is a widely studied research area. Until recently, research was focused on finding constant turbine inputs that optimize the performance of the wind farm. However, recent studies have shown that time-varying, dynamic inputs might be more beneficial. In this paper, the validity of this approach is further investigated by implementing it in scaled wind tunnel experiments and assessing load effects, showing promising results.
Steffen Raach, Bart Doekemeijer, Sjoerd Boersma, Jan-Willem van Wingerden, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-54, https://doi.org/10.5194/wes-2019-54, 2019
Publication in WES not foreseen
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The presented work combines two control approaches of wake redirection control, feedforward wake redirection and feedback wake redirction. In our previous investigatins the lidar-assisted feedback control was studied and the advantages and disadvantages were discussed. The optimal yaw angles for the wind turbines are precomputed, the feedback takes care of uncertainties and disturbances. The concept is demonstrated in a high fidelity simulation model.
Hector Mendez Reyes, Stoyan Kanev, Bart Doekemeijer, and Jan-Willem van Wingerden
Wind Energ. Sci., 4, 549–561, https://doi.org/10.5194/wes-4-549-2019, https://doi.org/10.5194/wes-4-549-2019, 2019
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Within wind farms, the wind turbines interact with each other through their wakes. Turbines operating in these wakes have lower power production and increased wear and tear. Wake redirection is control strategy to steer the wakes aside from downstream turbines, increasing the power yield of the farm. Models for predicting the power gain and impacts on wear exist, but they are still immature and require validation. The validation of such a model is the purpose of this paper.
Andreas Rott, Bart Doekemeijer, Janna Kristina Seifert, Jan-Willem van Wingerden, and Martin Kühn
Wind Energ. Sci., 3, 869–882, https://doi.org/10.5194/wes-3-869-2018, https://doi.org/10.5194/wes-3-869-2018, 2018
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Active wake deflection (AWD) aims to increase the power output of a wind farm by misaligning the yaw of upstream turbines. We analysed the effect of dynamic wind direction changes on AWD. The results show that AWD is very sensitive towards these dynamics. Therefore, we present a robust active wake control, which considers uncertainties and wind direction changes, increasing the overall power output of a wind farm. A side effect is a significant reduction of the yaw actuation of the turbines.
Bart M. Doekemeijer, Sjoerd Boersma, Lucy Y. Pao, Torben Knudsen, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 749–765, https://doi.org/10.5194/wes-3-749-2018, https://doi.org/10.5194/wes-3-749-2018, 2018
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Most wind farm control algorithms in the literature rely on a simplified mathematical model that requires constant calibration to the current conditions. This paper provides such an estimation algorithm for a dynamic model capturing the turbine power production and flow field at hub height. Performance was demonstrated in high-fidelity simulations for two-turbine and nine-turbine farms, accurately estimating the ambient conditions and wind field inside the farms at a low computational cost.
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.
Sjoerd Boersma, Bart Doekemeijer, Mehdi Vali, Johan Meyers, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 75–95, https://doi.org/10.5194/wes-3-75-2018, https://doi.org/10.5194/wes-3-75-2018, 2018
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Controlling the flow within wind farms to reduce the fatigue loads and provide grid facilities such as the delivery of a demanded power is a challenging control problem due to the underlying time-varying non-linear wake dynamics. In this paper, a control-oriented dynamical wind farm model is presented and validated with high-fidelity wind farm models. In contrast to the latter models, the model presented in this work is computationally efficient and hence suitable for online wind farm control.
Cian J. Desmond, Simon J. Watson, Christiane Montavon, and Jimmy Murphy
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2017-34, https://doi.org/10.5194/wes-2017-34, 2017
Revised manuscript not accepted
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The flow over densely forested terrain under neutral and non-neutral conditions is considered using commercially available Computational Fluid Dynamics software. Results are validated against data from a site in North-Eastern France. It is shown that the effects of both neutral and stable atmospheric stratifications can be modelled numerically using state of the art methodologies whilst unstable stratifications remain elusive.
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.
Sachin T. Navalkar, Lars O. Bernhammer, Jurij Sodja, Edwin van Solingen, Gijs A. M. van Kuik, and Jan-Willem van Wingerden
Wind Energ. Sci., 1, 205–220, https://doi.org/10.5194/wes-1-205-2016, https://doi.org/10.5194/wes-1-205-2016, 2016
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In order to reduce the cost of wind energy, it is necessary to reduce the loads that wind turbines withstand over their lifetime. The combination of blade rotation with newly designed blade shape changing actuators is demonstrated experimentally. While load reduction is achieved, the additional flexibility implies that careful control design is needed to avoid instability.
G. A. M. van Kuik, J. Peinke, R. Nijssen, D. Lekou, J. Mann, J. N. Sørensen, C. Ferreira, J. W. van Wingerden, D. Schlipf, P. Gebraad, H. Polinder, A. Abrahamsen, G. J. W. van Bussel, J. D. Sørensen, P. Tavner, C. L. Bottasso, M. Muskulus, D. Matha, H. J. Lindeboom, S. Degraer, O. Kramer, S. Lehnhoff, M. Sonnenschein, P. E. Sørensen, R. W. Künneke, P. E. Morthorst, and K. Skytte
Wind Energ. Sci., 1, 1–39, https://doi.org/10.5194/wes-1-1-2016, https://doi.org/10.5194/wes-1-1-2016, 2016
Related subject area
Thematic area: Dynamics and control | Topic: Wind turbine control
COFLEX: a novel set point optimiser and feedforward–feedback control scheme for large, flexible wind turbines
Control strategies for multi-rotor wind turbines
LIDAR-assisted nonlinear output regulation of wind turbines for fatigue load reduction
Output-constrained individual pitch control methods using the multiblade coordinate transformation: Trading off actuation effort and blade fatigue load reduction for wind turbines
Obtaining fatigue-based frequency domain specifications for the design of controllers in wind turbines
Brief communication: Real-time estimation of the optimal tip-speed ratio for controlling wind turbines with degraded blades
On the robustness of a blade-load-based wind speed estimator to dynamic pitch control strategies
The potential of wave feedforward control for floating wind turbines: a wave tank experiment
Assessing the impact of waves and platform dynamics on floating wind-turbine energy production
Combining wake redirection and derating strategies in a load-constrained wind farm power maximization
Feedforward pitch control for a 15 MW wind turbine using a spinner-mounted single-beam lidar
Wind vane correction during yaw misalignment for horizontal-axis wind turbines
Increased power gains from wake steering control using preview wind direction information
Analysis and multi-objective optimisation of wind turbine torque control strategies
Damping analysis of floating offshore wind turbines (FOWTs): a new control strategy reducing the platform vibrations
Assessing lidar-assisted feedforward and multivariable feedback controls for large floating wind turbines
Prognostics-based adaptive control strategy for lifetime control of wind turbines
Platform yaw drift in upwind floating wind turbines with single-point-mooring system and its mitigation by individual pitch control
Evaluation of lidar-assisted wind turbine control under various turbulence characteristics
FarmConners wind farm flow control benchmark – Part 1: Blind test results
Demonstration of a fault impact reduction control module for wind turbines
Lidar-assisted model predictive control of wind turbine fatigue via online rainflow counting considering stress history
Guido Lazzerini, Jacob Deleuran Grunnet, Tobias Gybel Hovgaard, Fabio Caponetti, Vasu Datta Madireddi, Delphine De Tavernier, and Sebastiaan Paul Mulders
Wind Energ. Sci., 10, 1303–1327, https://doi.org/10.5194/wes-10-1303-2025, https://doi.org/10.5194/wes-10-1303-2025, 2025
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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 a feedforward–feedback control scheme, addressing flexibility and overcoming the limitations of conventional strategies. By testing it on a large-scale reference turbine with realistic wind conditions, we demonstrated improvements to power by up to 5 % while constraining blade deflections.
Finn Matras and Morten Dinhoff Pedersen
Wind Energ. Sci., 10, 925–939, https://doi.org/10.5194/wes-10-925-2025, https://doi.org/10.5194/wes-10-925-2025, 2025
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Wind energy has been dominated by ever-increasing single-rotor wind turbines. Structural scaling laws make multi-rotor wind turbines attractive, as they can achieve similar power outputs but work with, rather than against, scaling laws. This work investigates high-level control strategies for a 23-rotor multi-rotor wind turbine, including the aerodynamic interactions between the rotors, and suggests an alternative to pitch control using multi-rotor furling.
Robert H. Moldenhauer and Robert Schmid
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-184, https://doi.org/10.5194/wes-2024-184, 2025
Revised manuscript accepted for WES
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We explore the application of a novel wind turbine control methodology for maximizing power generation while also improving the turbine's lifespan by reducing fatigue load on the tower structure, blades, and rotor. Simulations of the controller against a state-of-the-art baseline controller on a 15-MW reference turbine showed that the controller improves on all considered performance metrics. The reductions were achieved without sacrificing power generation or tracking performance.
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.
Irene Miquelez-Madariaga, Jesús Arellano, Daniel Lacheta-Lecumberri, and Jorge Elso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-154, https://doi.org/10.5194/wes-2024-154, 2024
Revised manuscript accepted for WES
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This paper presents a method for obtaining mechanical fatigue estimations from linear models and fatigue-based specifications for the design of controllers for wind turbines. The method is validated by the design of controllers for a 15 MW reference wind turbine. The error in the fatigue estimation is smaller than 2 % and fatigue loads are successfully reduced.
Devesh Kumar and Mario A. Rotea
Wind Energ. Sci., 9, 2133–2146, https://doi.org/10.5194/wes-9-2133-2024, https://doi.org/10.5194/wes-9-2133-2024, 2024
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The performance of a wind turbine is affected by blade surface degradation due to wear and tear, dirt, bugs, and icing. As blades degrade, optimal operating points such as the tip-speed ratio (TSR) can change. Re-tuning the TSR to its new optimal value can lead to recovery of energy losses under blade degradation. In this work, we utilize a real-time algorithm to re-tune the TSR to its new unknown optimal value under blade degradation and demonstrate energy gains using simulations.
Marion Coquelet, Maxime Lejeune, Laurent Bricteux, Aemilius A. W. van Vondelen, Jan-Willem van Wingerden, and Philippe Chatelain
Wind Energ. Sci., 9, 1923–1940, https://doi.org/10.5194/wes-9-1923-2024, https://doi.org/10.5194/wes-9-1923-2024, 2024
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An extended Kalman filter is used to estimate the wind impinging on a wind turbine based on the blade bending moments and a turbine model. Using large-eddy simulations, this paper verifies how robust the estimator is to the turbine control strategy as it impacts loads and operating parameters. It is shown that including dynamics in the turbine model to account for delays between actuation and bending moments is needed to maintain the accuracy of the estimator when dynamic pitch control is used.
Amr Hegazy, Peter Naaijen, Vincent Leroy, Félicien Bonnefoy, Mohammad Rasool Mojallizadeh, Yves Pérignon, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 1669–1688, https://doi.org/10.5194/wes-9-1669-2024, https://doi.org/10.5194/wes-9-1669-2024, 2024
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Successful wave tank experiments were conducted to evaluate the feedforward (FF) control strategy benefits in terms of structural loads and power quality of floating wind turbine components. The wave FF control strategy is effective when it comes to alleviating the effects of the wave forces on the floating offshore wind turbines, whereas wave FF control requires a significant amount of actuation to minimize the platform pitch motion, which makes such technology unfavorable for that objective.
Alessandro Fontanella, Giorgio Colpani, Marco De Pascali, Sara Muggiasca, and Marco Belloli
Wind Energ. Sci., 9, 1393–1417, https://doi.org/10.5194/wes-9-1393-2024, https://doi.org/10.5194/wes-9-1393-2024, 2024
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Waves can boost a floating wind turbine's power output by moving its rotor against the wind. Studying this, we used four models to explore the impact of waves and platform dynamics on turbines in the Mediterranean. We found that wind turbulence, not waves, primarily affects power fluctuations. In real conditions, floating wind turbines produce less energy compared to fixed-bottom ones, mainly due to platform tilt.
Alessandro Croce, Stefano Cacciola, and Federico Isella
Wind Energ. Sci., 9, 1211–1227, https://doi.org/10.5194/wes-9-1211-2024, https://doi.org/10.5194/wes-9-1211-2024, 2024
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For a few years now, various techniques have been studied to maximize the energy production of a wind farm, that is, from a system consisting of several wind turbines. These wind farm controller techniques are often analyzed individually and can generate loads higher than the design ones on the individual wind turbine. In this paper we study the simultaneous use of two different techniques with the goal of finding the optimal combination that at the same time preserves the design loads.
Wei Fu, Feng Guo, David Schlipf, and Alfredo Peña
Wind Energ. Sci., 8, 1893–1907, https://doi.org/10.5194/wes-8-1893-2023, https://doi.org/10.5194/wes-8-1893-2023, 2023
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A high-quality preview of the rotor-effective wind speed is a key element of the benefits of feedforward pitch control. We model a one-beam lidar in the spinner of a 15 MW wind turbine. The lidar rotates with the wind turbine and scans the inflow in a circular pattern, mimicking a multiple-beam lidar at a lower cost. We found that a spinner-based one-beam lidar provides many more control benefits than the one on the nacelle, which is similar to a four-beam nacelle lidar for feedforward control.
Andreas Rott, Leo Höning, Paul Hulsman, Laura J. Lukassen, Christof Moldenhauer, and Martin Kühn
Wind Energ. Sci., 8, 1755–1770, https://doi.org/10.5194/wes-8-1755-2023, https://doi.org/10.5194/wes-8-1755-2023, 2023
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This study examines wind vane measurements of commercial wind turbines and their impact on yaw control. The authors discovered that rotor interference can cause an overestimation of wind vane measurements, leading to overcorrection of the yaw controller. A correction function that improves the yaw behaviour is presented and validated in free-field experiments on a commercial wind turbine. This work provides new insights into wind direction measurements and suggests ways to optimize yaw control.
Balthazar Arnoldus Maria Sengers, Andreas Rott, Eric Simley, Michael Sinner, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 8, 1693–1710, https://doi.org/10.5194/wes-8-1693-2023, https://doi.org/10.5194/wes-8-1693-2023, 2023
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Unexpected wind direction changes are undesirable, especially when performing wake steering. This study explores whether the yaw controller can benefit from accessing wind direction information before a change reaches the turbine. Results from two models with different fidelities demonstrate that wake steering can indeed benefit from preview information.
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.
Matteo Capaldo and Paul Mella
Wind Energ. Sci., 8, 1319–1339, https://doi.org/10.5194/wes-8-1319-2023, https://doi.org/10.5194/wes-8-1319-2023, 2023
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The controller impacts the movements, loads and yield of wind turbines.
Standard controllers are not adapted for floating, and they can lead to underperformances and overloads. New control strategies, considering the coupling between the floating dynamics and the rotor dynamics, are necessary to reduce platform movements and to improve performances. This work proposes a new control strategy adapted to floating wind, showing a reduction in loads without affecting the power production.
Feng Guo and David Schlipf
Wind Energ. Sci., 8, 1299–1317, https://doi.org/10.5194/wes-8-1299-2023, https://doi.org/10.5194/wes-8-1299-2023, 2023
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This paper assesses lidar-assisted collective pitch feedforward (LACPF) and multi-variable feedback (MVFB) controls for the IEA 15.0 MW reference turbine. The main contributions of this work include (a) optimizing a four-beam pulsed lidar for a large turbine, (b) optimal tuning of speed regulation gains and platform feedback gains for the MVFB and LACPF controllers, and (c) assessing the benefits of the two control strategies using realistic offshore turbulence spectral characteristics.
Edwin Kipchirchir, M. Hung Do, Jackson G. Njiri, and Dirk Söffker
Wind Energ. Sci., 8, 575–588, https://doi.org/10.5194/wes-8-575-2023, https://doi.org/10.5194/wes-8-575-2023, 2023
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In this work, an adaptive control strategy for controlling the lifetime of wind turbine components is proposed. Performance of the lifetime controller is adapted based on real-time health status of the rotor blades to guarantee a predefined lifetime. It shows promising results in lifetime control of blades without speed regulation and tower load mitigation trade-off. It can be applied in optimizing maintenance scheduling of wind farms, which increases reliability and reduces maintenance costs.
Iñaki Sandua-Fernández, Felipe Vittori, Raquel Martín-San-Román, Irene Eguinoa, and José Azcona-Armendáriz
Wind Energ. Sci., 8, 277–288, https://doi.org/10.5194/wes-8-277-2023, https://doi.org/10.5194/wes-8-277-2023, 2023
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This work analyses in detail the causes of the yaw drift in floating offshore wind turbines with a single-point-mooring system induced by an upwind wind turbine. The ability of an individual pitch control strategy based on yaw misalignment is demonstrated through simulations using the NREL 5 MW wind turbine mounted on a single-point-mooring version of the DeepCwind OC4 floating platform. This effect is considered to be relevant for all single-point-moored concepts.
Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 8, 149–171, https://doi.org/10.5194/wes-8-149-2023, https://doi.org/10.5194/wes-8-149-2023, 2023
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The benefits of lidar-assisted control are evaluated using both the Mann model and Kaimal model-based 4D turbulence, considering the variation of turbulence parameters. Simulations are performed for the above-rated mean wind speed, using the NREL 5.0 MW reference wind turbine and a four-beam lidar system. Using lidar-assisted control reduces the variations in rotor speed, pitch rate, tower base fore–aft bending moment, and electrical power significantly.
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.
Benjamin Anderson and Edward Baring-Gould
Wind Energ. Sci., 7, 1753–1769, https://doi.org/10.5194/wes-7-1753-2022, https://doi.org/10.5194/wes-7-1753-2022, 2022
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Our article proposes an easy-to-integrate wind turbine control module which mitigates wind turbine fault conditions and sends predictive information to the grid operator, all while maximizing power production. This gives the grid operator more time to react to faults with its dispatch decisions, easing the transition between different generators. This study aims to illustrate the controller’s functionality under various types of faults and highlight potential wind turbine and grid benefits.
Stefan Loew and Carlo L. Bottasso
Wind Energ. Sci., 7, 1605–1625, https://doi.org/10.5194/wes-7-1605-2022, https://doi.org/10.5194/wes-7-1605-2022, 2022
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This publication presents methods to improve the awareness and control of material fatigue for wind turbines. This is achieved by enhancing a sophisticated control algorithm which utilizes wind prediction information from a laser measurement device. The simulation results indicate that the novel algorithm significantly improves the economic performance of a wind turbine. This benefit is particularly high for situations when the prediction quality is low or the prediction time frame is short.
Cited articles
Afshari, A., Mojahed, M., and Yusuff, R.: Simple Additive Weighting Approach to Personnel Selection Problem, International Journal of Innovation, Management and Technology, 1, 511–515, 2010. a
Aures, W.: Procedure for calculating the sensory euphony of arbitrary sound signal [Berechnungsverfahren für den sensorischen Wohlklang beliebiger Schallsignale], Acustica, 59, 130–141, 1985. a
Bagočius, V., Zavadskas, E. K., and Turskis, Z.: Multi-person selection of the best wind turbine based on the multi-criteria integrated additive-multiplicative utility function, J. Civ. Eng. Manag., 20, 590–599, https://doi.org/10.3846/13923730.2014.932836, 2014. a
Balduzzi, F., Bianchini, A., Carnevale, E. A., Ferrari, L., and Magnani, S.: Feasibility analysis of a Darrieus vertical-axis wind turbine installation in the rooftop of a building, Appl. Energ., 97, 921–929, https://doi.org/10.1016/j.apenergy.2011.12.008, 2012. a, b
Bergami, L. and Gauanaa, M.: ATEFlap Aerodynamic Model: A Dynamic Stall Model Including the Effects of Trailing Edge Flap Deflection, Tech. rep., Technical University of Denmark, https://backend.orbit.dtu.dk/ws/portalfiles/portal/6599679/ris-r-1792.pdf (last access: 10 January 2024), 2012. a
Bianchini, A., Balduzzi, F., Rainbird, J. M., Peiro, J., Graham, J. M. R., Ferrara, G., and Ferrari, L.: An Experimental and Numerical Assessment of Airfoil Polars for Use in Darrieus Wind Turbines–Part I: Flow Curvature Effects, J. Eng. Gas Turb. Power, 138, 032602, https://doi.org/10.1115/1.4031269, 2015. a
Bianchini, A., Bangga, G., Baring-Gould, I., Croce, A., Cruz, J. I., Damiani, R., Erfort, G., Simao Ferreira, C., Infield, D., Nayeri, C. N., Pechlivanoglou, G., Runacres, M., Schepers, G., Summerville, B., Wood, D., and Orrell, A.: Current status and grand challenges for small wind turbine technology, Wind Energ. Sci., 7, 2003–2037, https://doi.org/10.5194/wes-7-2003-2022, 2022. a, b, c
Bonaccorso, F., Scelba, G., Consoli, A., and Muscato, G.: EKF – based MPPT control for vertical axis wind turbines, in: IECON 2011 – 37th Annual Conference of the IEEE Industrial Electronics Society, 7–10 November 2011, Melbourne, VIC, Australia, 3614–3619, https://doi.org/10.1109/IECON.2011.6119896, 2011. a, b
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
Botha, J. D. M., Shahroki, A., and Rice, H.: An implementation of an aeroacoustic prediction model for broadband noise from a vertical axis wind turbine using a CFD informed methodology, J. Sound Vib., 410, 389–415, https://doi.org/10.1016/j.jsv.2017.08.038, 2017. a
Brandetti, L.: Codes, data and plots underlying the publication: Multi-objective calibration of vertical-axis wind turbine controllers: balancing aero-servo-elastic performance and noise, Version 1, 4TU.ResearchData [code and data set], https://doi.org/10.4121/34b8d260-049a-4f7c-b3cd-60f1f4019696, 2024. a
Brandetti, L. and van den Berg, D.: QBlade 2.0.5.2 Matlab Tutorial, Tech. rep., https://doi.org/10.4121/22134710, 2023. a
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
Brandetti, L., Avallone, F., De Tavernier, D., LeBlanc, B., Simão Ferreira, C., and Casalino, D.: Assessment through high-fidelity simulations of a low-fidelity noise prediction tool for a vertical-axis wind turbine, J. Sound Vib., 547, 117486, https://doi.org/10.1016/j.jsv.2022.117486, 2023a. a, b, c, d, e
Brooks, T. F. and Burley, C. L.: Rotor broadband noise prediction with comparison to model data, J. Am. Helicopter Soc., 49, 28–42, 2004. a
Burton, T., Jenkings, N., Sharpe, D., and Bossanyi, E. A.: Wind Energy Handbook, John Wiley & Sons, Ltd, ISBN 9781119992714, https://doi.org/10.1002/9781119992714, 2001. a
Clifton-Smith, M. J.: Aerodynamic Noise Reduction for Small Wind Turbine Rotors, Wind Eng., 34, 403–420, https://doi.org/10.1260/0309-524X.3.4.403, 2010. a
Daniel, P. and Webber, R.: Psychoacoustical Roughness: Implementation of an Optimized Model, Acustica, 83, 113–23, 1997. a
Dayan, E.: Wind energy in buildings: Power generation from wind in the urban environment – where it is needed most, Refocus, 7, 33–38, https://doi.org/10.1016/S1471-0846(06)70545-5, 2006. a
De Tavernier, D. M. A.: Aerodynamic advances in vertical-axis wind turbines, PhD thesis, TU Delft, Delft, https://doi.org/10.4233/uuid:7086f01f-28e7-4e1b-bf97-bb3e38dd22b9, 2021. a, b
Di, G. Q., Chen, X. W., Song, K., Zhou, B., and Pei, C. M.: Improvement of Zwicker's psychoacoustic annoyance model aiming at tonal noises, Appl. Acoust., 105, 164–170, https://doi.org/10.1016/j.apacoust.2015.12.006, 2016. a, b, c
Eriksson, S., Bernhoff, H., and Leijon, M.: Evaluation of different turbine concepts for wind power, Renew. Sust. Energ. Rev., 12, 1419–1434, https://doi.org/10.1016/j.rser.2006.05.017, 2008. a, b
Eriksson, S., Kjellin, J., and Bernhoff, H.: Tip speed ratio control of a 200 kW VAWT with synchronous generator and variable DC voltage, Energy Sci. Eng., 1, 135–143, https://doi.org/10.1002/ese3.23, 2013. a
Fastl, H. and Zwicker, E.: Psychoacoustics – Facts and models, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-540-68888-4, 2007. a, b
Gambier, A.: Multiobjective Optimal Control of Wind Turbines: A Survey on Methods and Recommendations for the Implementation, Energies, 15, 567, https://doi.org/10.3390/en15020567, 2022. a
Greco, G. F., Merino-Martínez, R., and Osses, A.: SQAT: a MATLAB-based toolbox for quantitative sound quality analysis, in: Inter-Noise 2023: 52nd International Congress and Exposition on Noise Control Engineering Location of Conference, 20—23 August 2023, Chiba, Japan, 12 pp., https://doi.org/10.3397/IN_2023_1075, 2023a. a, b
Greco, G. F., Merino-Martínez, R., and Osses, A.: SQAT: a sound quality analysis toolbox for MATLAB, https://doi.org/10.5281/zenodo.7934709, 2023b. a, b
Haque, M. E., Negnevitsky, M., and Muttaqi, K. M.: A Novel Control Strategy for a Variable Speed Wind Turbine with a Permanent Magnet Synchronous Generator, in: 2008 IEEE Industry Applications Society Annual Meeting, 5–9 October 2008, Edmonton, AB, Canada, 1–8, https://doi.org/10.1109/08IAS.2008.374, 2008. a
Holley, W., Rock, S., and Chaney, K.: Control of variable speed wind turbines below-rated wind speed, Proceedings of the 3rd ASME/JSME Conference, ASME/JSME, 18–23 July 1999, San Francisco, California, USA, https://search.worldcat.org/title/42269277 2023 (last access: 15 September 2023), 1999. a
Howell, R., Qin, N., Edwards, J., and Durrani, N.: Wind tunnel and numerical study of a small vertical axis wind turbine, Renew. Energ., 35, 412–422, https://doi.org/10.1016/j.renene.2009.07.025, 2010. a
Hutchinson, M. and Zhao, F.: Global Wind Report 2023, Tech. rep., https://gwec.net/wp-content/uploads/2023/03/GWR-2023_interactive_v2_compressed.pdf (last access: 15 June 2023), 2023. a
International Organization for Standardization: ISO norm 532–1 – Acoustics – Method for calculating loudness – Zwicker method, Tech. rep., https://www.iso.org/obp/ui/en/#iso:std:iso:532:-1:ed-1:v2:en (last access: 28 December 2023), 2017. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5 MW Reference Wind Turbine for Offshore System Development, Tech. rep., NREL/TP-500-38060, NREL, https://www.nrel.gov/docs/fy09osti/38060.pdf (last access: 18 May 2023), 2009. a
Khan, M., Alavi, M., Mohan, N., Azeez, A., Shanif, A., and Javed, B.: Wind Turbine design and fabrication to power street lights, in: vol. 108, 25–27 February 2017, Malacca, Malaysia, https://doi.org/10.1051/matecconf/201710808010, 2017. a
Kim, J. W., Haeri, S., and Joseph, P. F.: On the reduction of aerofoil-turbulence interaction noise associated with wavy leading edges, J. Fluid Mech., 792, 526–552, https://doi.org/10.1017/jfm.2016.95, 2016. a
Klok, C. W., Kirkels, A. F., and Alkemade, F.: Impacts, procedural processes, and local context: Rethinking the social acceptance of wind energy projects in the Netherlands, Energy Research & Social Science, 99, 103044, https://doi.org/10.1016/j.erss.2023.103044, 2023. a
Lao, Y., Rotea, M. A., Koeln, J. P., Sakib, M. S., and Griffith, D. T.: Economic Nonlinear Model Predictive Control of Offshore Vertical-Axis Wind Turbines, 2022 American Control Conference (ACC), 8–10 June 2022, Atlanta, GA, USA, 3518–3525, https://doi.org/10.23919/ACC53348.2022.9867846, 2022. a
Lara, M., Garrido, J., Ruz, M. L., and Vázquez, F.: Multi-objective optimization for simultaneously designing active control of tower vibrations and power control in wind turbines, Energy Reports, 9, 1637–1650, https://doi.org/10.1016/j.egyr.2022.12.141, 2023a. a, b, c
Lara, M., Garrido, J., van Wingerden, J. W., Mulders, S. P., and Vázquez, F.: Optimization with genetic algorithms of individual pitch control design with and without azimuth offset for wind turbines in the full load region, in: 22nd IFAC World Congress, 9–14 July 2023, Yokohama, Japan, 375–380, https://doi.org/10.1016/j.ifacol.2023.10.1591, 2023b. a
LeBlanc, B. and Simão Ferreira, C.: Estimation of blade loads for a variable pitch vertical axis wind turbine from particle image velocimetry, Wind Energy, 25, 313–332, https://doi.org/10.1002/we.2674, 2021. a, b, c, d
LeBlanc, B. and Simão Ferreira, C.: Estimation of blade loads for a variable pitch Vertical Axis Wind Turbine with strain gage measurements, Wind Energy, 25, 1030–1045, https://doi.org/10.1002/we.2713, 2022. a
Lee, H.-C. and Chang, C.-T.: Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan, Renew. Sust. Energ. Rev., 92, 883–896, https://doi.org/10.1016/j.rser.2018.05.007, 2018. a
Leishman, J. and Beddoes, T.: A Semi-Empirical Model for Dynamic Stall, J. Am. Helicopter Soc., 34, 3–17, https://doi.org/10.4050/JAHS.34.3.3, 1989. a
Liu, Y., Pamososuryo, A. K., Ferrari, R. M. G., and van Wingerden, J. W.: The Immersion and Invariance Wind Speed Estimator Revisited and New Results, IEEE Control Systems Letters, 6, 361–366, https://doi.org/10.1109/LCSYS.2021.3076040, 2022. a
Lowson, M. V. and Ollerhead, J. B.: A theoretical study of helicopter noise, J. Sound Vib., 9, 197–222, 1969. a
Lukovic, M. K., Tian, Y., and Matusik, W.: Diversity-guided multi-objective Bayesian optimization with batch evaluations, in: 34th Conference on Neural Information Processing Systems, 6–12 December 2020, Vancouver, Canada, https://proceedings.neurips.cc/paper_files/paper/2020/file/cd3109c63bf4323e6b987a5923becb96-Paper.pdf (last access: 31 August 2023), 2020. a, b
Madsen, H. A.: The Actuator Cylinder – A Flow Model for Vertical Axis Wind Turbines, PhD thesis, https://doi.org/10.13140/RG.2.1.2512.3040, 1982. a
Maillard, J., Bresciani, A. P. C., and Finez, A.: Perceptual validation of wind turbine noise auralization, in: Proceedings of the 10th Convention of the European Acoustics Association (Forum Acusticum), 11–15 September 2023, Turin, Italy, https://doi.org/10.61782/fa.2023.0444, 2023. a, b
Marten, D., Saverin, J., Behrens de Luna, R., and Perez-Becker, S.: QBlade documentation, Tech. rep., https://docs.qblade.org/ (last access: 27 December 2023), 2021. a
Mertens, S., van Kuik, G., and van Bussel, G.: Performance of an H-Darrieus in the Skewed Flow on a Roof, J. Sol. Energ.-T. ASME, 125, 433–440, https://doi.org/10.1115/1.1629309, 2003. a
Miettinen, K.: Nonlinear multiobjective optimization, Springer, https://doi.org/10.1007/978-1-4615-5563-6, 1999. a
Montgomerie, B.: Methods for Root Effects, Tip Effects and Extending the Angle of Attack Range to 6180, With Application to Aerodynamics for Blades on Wind Turbines and Propellers, Tech. rep., Swedish Defence Research Agency, https://www.foi.se/rest-api/report/FOI-R--1305--SE (last access: 20 August 2023), 2004. a
Moriarty, P. and Migliore, P.: Semi-Empirical Aeroacoustic Noise Prediction Code for Wind Turbines, Tech. rep., NREL – National Renewable Energy Laboratory, https://www.nrel.gov/docs/fy04osti/34478.pdf (last access: 30 December 2023), 2003. a
Moustakis, N., Mulders, S. P., Kober, J., and van Wingerden, J. W.: A Practical Bayesian Optimization Approach for the Optimal Estimation of the Rotor Effective Wind Speed, in: 2019 American Control Conference (ACC), 10–12 July 2019, Philadelphia, PA, USA, 4179–4185, https://doi.org/10.23919/ACC.2019.8814622, 2019. a
Odgaard, P. F., Larsen, L. F. S., Wisniewski, R., and Hovgaard, T. G.: On using Pareto optimality to tune a linear model predictive controller for wind turbines, Renew. Energ., 87, 884–891, https://doi.org/10.1016/j.renene.2015.09.067, 2016. 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
Osses Vecchi, A., García León, R., and Kohlrausch, A.: Modelling the sensation of fluctuation strength, in: vol. 28 of Proceedings of Meetings on Acoustics, 5–9 September 2016, Buenos Aires, Brazil, https://doi.org/10.1121/2.0000410, 2016. a
Østergaard, K. Z., Brath, P., and Stoustrup, J.: Estimation of effective wind speed, J. Phys. Conf. Ser., 75, 012082, https://doi.org/10.1088/1742-6596/75/1/012082, 2007. a
Papi, F., Nocentini, A., Ferrara, G., and Bianchini, A.: On the Use of Modern Engineering Codes for Designing a Small Wind Turbine: An Annotated Case Study, Energies, 14, 1013, https://doi.org/10.3390/en14041013, 2021. a
Pieren, R., Heutschi, K., Müller, M., Manyoky, M., and Eggenschwiler, K.: Auralization of Wind Turbine Noise: Emission Synthesis, Acta Acust. United Ac., 100, 25–33, https://doi.org/10.3813/AAA.918683, 2014. a
Pieren, R., Bertsch, L., Lauper, D., and Schäffer, B.: Improving Future Low-noise Aircraft Technologies Using Experimental Perception-Based Evaluation of Synthetic Flyovers, Sci. Total Environ., 692, 68–81, https://doi.org/10.1016/j.scitotenv.2019.07.253, 2019. a, b
Poulsen, A. H., Raaschou-Nielsen, O., Peña, A., Hahmann, A. N., Nordsborg, R. B., Ketzel, M., Brandt, J., and Sørensen, M.: Impact of Long-Term Exposure to Wind Turbine Noise on Redemption of Sleep Medication and Antidepressants: A Nationwide Cohort Study, Environ. Health Persp., 127, 037005, https://doi.org/10.1289/EHP3909, 2019. a
Pourrajabian, A., Rahgozar, S., Dehghan, M., and Wood, D.: A comprehensive multi-objective optimization study for the aerodynamic noise mitigation of a small wind turbine, Eng. Anal. Bound. Elem., 155, 553–564, https://doi.org/10.1016/j.enganabound.2023.06.035, 2023. a
Ramirez, L.: Offshore wind energy: 2023 statistics, Tech. rep., https://windeurope.org/intelligence-platform/product/offshore-wind-in-europe-key-trends-and-statistics-2022/ (last access: 15 August 2023), 2023. a
Rogers, A. L., Manwell, J. F., and Wright, S.: Wind turbine acoustic noise, Tech. rep., https://doi.org/10.1260/0957456042872777, 2006. a, b
Ruijgrok, G. J. J.: Elements of Aviation Acoustics, Cambridge University Press, https://doi.org/10.1017/S0001924000027056, 1993. a
Santín, I., Pedret, C., and Vilanova, R.: Control and Decision Strategies in Wastewater Treatment Plants for Operation Improvement, Intelligent Systems, Springer, https://doi.org/10.1007/978-3-319-46367-4, 2017. a
Sessarego, M. and Wood, D.: Multi-dimensional optimization of small wind turbine blades, Renewables: Wind, Water, and Solar, 9, 9, https://doi.org/10.1186/s40807-015-0009-x, 2015. a
Simão Ferreira, C. J., van Kuik, G., van Bussel, G., and Scarano, F.: Visualization by PIV of dynamic stall on a vertical axis wind turbine, Exp. Fluids, 97–108, https://doi.org/10.1007/s00348-008-0543-z, 2009. a
Soltani, M. N., Knudsen, T., Svenstrup, M., Wisniewski, R., Brath, P., Ortega, R., and Johnson, K.: 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
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., Rodrigo, J. S., Sempreviva, A. M., Smith, J. C., Tuohy, A., and Wiser, R.: Grand challenges in the science of wind energy, Science, 366, 6464, https://doi.org/10.1126/science.aau2027, 2019. a, b
von Bismarck, G.: Sharpness as an attribute of the timbre of steady sounds, Acustica, 30, 159–172, 1974. a
Vorländer, M.: Auralization – Fundamentals of Acoustics, Modelling, Simulation, Algorithms and Acoustic Virtual Reality, Springer, ISBN 978-3-540-48830-9, https://doi.org/10.1007/978-3-540-48830-9, 2008. a, b, c
Wang, P., Zhu, Z., and Wang, Y.: A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design, Inform. Sciences, 345, 27–45, https://doi.org/10.1016/j.ins.2016.01.076, 2016. a, b, c
Watson, S., Moro, A., Reis, V., Baniotopoulos, C., Barth, S., Bartoli, G., Bauer, F., Boelman, E., Bosse, D., Cherubini, A., Croce, A., Fagiano, L., Fontana, M., Gambier, A., Gkoumas, K., Golightly, C., Latour, M. I., Jamieson, P., Kaldellis, J., Macdonald, A., Murphy, J., Muskulus, M., Petrini, F., Pigolotti, L., Rasmussen, F., Schild, P., Schmehl, R., Stavridou, N., Tande, J., Taylor, N., Telsnig, T., and Wiser, R.: Future emerging technologies in the wind power sector: A European perspective, Renew. Sust. Energ. Rev., 113, 109270, https://doi.org/10.1016/j.rser.2019.109270, 2019. a, b, c
Short summary
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.
This research presents a multi-objective optimisation approach to balance vertical-axis wind...
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