Articles | Volume 8, issue 8
https://doi.org/10.5194/wes-8-1299-2023
© Author(s) 2023. 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-8-1299-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Aug 2023
Research article |
| 24 Aug 2023
| 24 Aug 2023
Assessing lidar-assisted feedforward and multivariable feedback controls for large floating wind turbines
Wind Energy Technology Institute, Flensburg University of Applied Sciences, Kanzleistraße 91–93, 24943 Flensburg, Germany
David Schlipf
Wind Energy Technology Institute, Flensburg University of Applied Sciences, Kanzleistraße 91–93, 24943 Flensburg, Germany
Related authors
Zhaoyu Zhang, Feng Guo, David Schlipf, Paolo Schito, and Alberto Zasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-162, https://doi.org/10.5194/wes-2023-162, 2024
Preprint withdrawn
Short summary
Short summary
This paper aims to analyse the uncertainty in wind direction estimation of LIDAR and to improve the estimation accuracy. Findings demonstrate that this LIDAR estimation method is insufficient to supervise the turbine yaw control system in terms of both accuracy and timeliness. Future research should apply more advanced wind flow models to explore more accurate wind field reconstruction methods.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Yiyin Chen, Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 7, 539–558, https://doi.org/10.5194/wes-7-539-2022, https://doi.org/10.5194/wes-7-539-2022, 2022
Short summary
Short summary
Lidar-assisted control of wind turbines requires a wind field generator capable of simulating wind evolution. Out of this need, we extend the Veers method for 3D wind field generation to 4D and propose a two-step Cholesky decomposition approach. Based on this, we develop a 4D wind field generator – evoTurb – coupled with TurbSim and Mann turbulence generator. We further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide practical suggestions.
Zhaoyu Zhang, Feng Guo, David Schlipf, Paolo Schito, and Alberto Zasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-162, https://doi.org/10.5194/wes-2023-162, 2024
Preprint withdrawn
Short summary
Short summary
This paper aims to analyse the uncertainty in wind direction estimation of LIDAR and to improve the estimation accuracy. Findings demonstrate that this LIDAR estimation method is insufficient to supervise the turbine yaw control system in terms of both accuracy and timeliness. Future research should apply more advanced wind flow models to explore more accurate wind field reconstruction methods.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Yiyin Chen, Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 7, 539–558, https://doi.org/10.5194/wes-7-539-2022, https://doi.org/10.5194/wes-7-539-2022, 2022
Short summary
Short summary
Lidar-assisted control of wind turbines requires a wind field generator capable of simulating wind evolution. Out of this need, we extend the Veers method for 3D wind field generation to 4D and propose a two-step Cholesky decomposition approach. Based on this, we develop a 4D wind field generator – evoTurb – coupled with TurbSim and Mann turbulence generator. We further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide practical suggestions.
Yiyin Chen, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 6, 61–91, https://doi.org/10.5194/wes-6-61-2021, https://doi.org/10.5194/wes-6-61-2021, 2021
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Wind evolution is currently of high interest, mainly due to the development of lidar-assisted wind turbine control (LAC). Moreover, 4D stochastic wind field simulations can be made possible by integrating wind evolution into 3D simulations to provide a more realistic simulation environment for LAC. Motivated by these factors, we investigate the potential of Gaussian process regression in the parameterization of a two-parameter wind evolution model using data of two nacelle-mounted lidars.
Jennifer Annoni, Paul Fleming, Andrew Scholbrock, Jason Roadman, Scott Dana, Christiane Adcock, Fernando Porte-Agel, Steffen Raach, Florian Haizmann, and David Schlipf
Wind Energ. Sci., 3, 819–831, https://doi.org/10.5194/wes-3-819-2018, https://doi.org/10.5194/wes-3-819-2018, 2018
Short summary
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This paper addresses the modeling aspect of wind farm control. To implement successful wind farm controls, a suitable model has to be used that captures the relevant physics. This paper addresses three different wake models that can be used for controls and compares these models with lidar field data from a utility-scale turbine.
Antoine Borraccino, David Schlipf, Florian Haizmann, and Rozenn Wagner
Wind Energ. Sci., 2, 269–283, https://doi.org/10.5194/wes-2-269-2017, https://doi.org/10.5194/wes-2-269-2017, 2017
Short summary
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The paper describes an innovative methodology to extract useful wind field estimates from remote sensing instruments – called lidars – mounted on the nacelle of a wind turbine. We used lidar measurements at multiple distances upstream and close to the wind turbine rotor in order to retrieve free-stream wind characteristics. The methodology and the obtained results open new paths to assess the power performance of wind turbines, which is essential to ensure financially viable wind farm projects.
Steffen Raach, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 2, 257–267, https://doi.org/10.5194/wes-2-257-2017, https://doi.org/10.5194/wes-2-257-2017, 2017
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This work provides a possible solution to closed-loop flow control in a wind farm.
The remote sensing technology, lidar, which is a laser-based measurement system, is used to obtain wind speed information behind a wind turbine. The measurements are processed using a model-based approach to estimate position information of the wake. The information is then used in a controller to redirect the wake to the desired position. Altogether, the concept aims to increase the power output of a wind farm.
Juan José Trujillo, Janna Kristina Seifert, Ines Würth, David Schlipf, and Martin Kühn
Wind Energ. Sci., 1, 41–53, https://doi.org/10.5194/wes-1-41-2016, https://doi.org/10.5194/wes-1-41-2016, 2016
Short summary
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We present the analysis of the trajectories followed by the wind, in the immediate vicinity, behind an offshore wind turbine and their dependence on its yaw misalignment. We apply wake tracking on wind fields measured with a lidar (light detection and ranging) system located at the nacelle of the wind turbine and pointing downstream. The analysis reveals discrepancies of the estimated mean wake paths against theoretical and wind tunnel experiments using different wake-tracking techniques.
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
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
On the robustness of a blade load-based wind speed estimator to dynamic pitch control strategies
Multi-objective calibration of vertical-axis wind turbine controllers: balancing aero-servo-elastic performance and noise
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
Brief communication: Real-time estimation of optimal tip-speed ratio for controlling wind turbines with degraded blades
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
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
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.
Marion Coquelet, Maxime Lejeune, Laurent Bricteux, Aemilius A. W. van Vondelen, Jan-Willem van Wingerden, and Philippe Chatelain
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-56, https://doi.org/10.5194/wes-2024-56, 2024
Revised manuscript accepted for WES
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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.
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.
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
Short summary
Short summary
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.
Devesh Kumar and Mario Rotea
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-144, https://doi.org/10.5194/wes-2023-144, 2023
Revised manuscript accepted for WES
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The performance of a wind turbine is affected by blade surface degradation due to wear and tear, dirt, bugs, 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 gradient-based algorithm to retune the TSR to its new unknown optimal value under blade degradation and demonstrate energy gains using simulations.
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.
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
Short summary
Short summary
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
Allen, C., Viscelli, A., Dagher, H., Goupee, A., Gaertner, E., Abbas, N., Hall,
M., and Barter, G.: Definition of the UMaine VolturnUS-S Reference Platform
Developed for the IEA Wind 15-Megawatt Offshore Reference Wind Turbine, Tech.
Rep. NREL/TP-5000-76773, NREL, https://doi.org/10.2172/1660012, 2020. a, b
Barrera, C., Battistella, T., Guanche, R., and Losada, I. J.: Mooring system
fatigue analysis of a floating offshore wind turbine, Ocean Eng., 195,
106670, https://doi.org/10.1016/j.oceaneng.2019.106670, 2020. a
Barter, G., Bortolotti, P., Gaertner, E., Abbas, N. J., dzalkind, Rinker, J., Zahle, F., T-Wainwright, Branlard, E., Wang, L., Padrón, L. A., and Hall, M.: IEAWindTask37/IEA-15-240-RWT: v1.1.6: HAWC2 monopile, VolturnUS-S CAD model, Zenodo [data set], https://doi.org/10.5281/zenodo.8070464, 2023.
Bossanyi, E. A., Kumar, A., and Hugues-Salas, O.: Wind turbine control
applications of turbine-mounted LIDAR, J. Phys. Conf. Ser.,
555, 012011, https://doi.org/10.1088/1742-6596/555/1/012011, 2014. a
Bredmose, H., Lemmer, F., Borg, M., Pegalajar-Jurado, A., Mikkelsen, R.,
Larsen, T. S., Fjelstrup, T., Yu, W., Lomholt, A., Boehm, L., and Armendariz,
J. A.: The Triple Spar campaign: Model tests of a 10MW floating wind turbine
with waves, wind and pitch control, Enrgy. Proced., 137, 58–76,
https://doi.org/10.1016/j.egypro.2017.10.334, 2017. a
Catapult, O.: Floating Offshore Wind: Cost Reduction Pathways to Subsidy Free,
Tech. rep., Catapult, Offshore Renewable Energy, https://ore.catapult.org.uk/wp-content/uploads/2021/01/FOW-Cost-Reduction-Pathways-to-Subsidy-Free-report-.pdf (last access: 12 August 2023), 2021. a
Cheynet, E., Jakobsen, J. B., and Obhrai, C.: Spectral characteristics of
surface-layer turbulence in the North Sea, Enrgy. Proced., 137, 414–427,
https://doi.org/10.1016/j.egypro.2017.10.366, 2017. a
de Maré, M. and Mann, J.: Validation of the Mann spectral tensor for offshore
wind conditions at different atmospheric stabilities, J. Phys.
Conf. Ser., 524, 012106, https://doi.org/10.1088/1742-6596/524/1/012106, 2014. a
DNV-GL: Bladed theory manual: version 4.8, Tech. rep., Garrad Hassan &
Partners Ltd., Bristol, UK, 2016. a
Fleming, P. A., Peiffer, A., and Schlipf, D.: Wind turbine controller to
mitigate structural loads on a floating wind turbine platform, J.
Offshore Mech. Arct., 141, 061901, https://doi.org/10.1115/1.4042938,
2019. a, b
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter,
G. E., Abbas, N. J., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G. N.,
Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.:
IEA Wind TCP Task 37: Definition of the IEA 15-Megawatt Offshore Reference
Wind Turbine, Technical Report, https://doi.org/10.2172/1603478, 2020. a, b
Guo, F., Mann, J., Peña, A., Schlipf, D., and Cheng, P. W.: The space-time
structure of turbulence for lidar-assisted wind turbine control, Renew.
Energ., 195, 293–310, https://doi.org/10.1016/j.renene.2022.05.133,
2022a. a, b, c, d
Guo, F., Schlipf, D., Zhu, H., Platt, A., Cheng, P. W., and Thomas, F.: Updates
on the OpenFAST Lidar Simulator, J. Phys. Conf. Ser.,
2265, 042030, https://doi.org/10.1088/1742-6596/2265/4/042030, 2022b. a
Held, D. P. and Mann, J.: Lidar estimation of rotor-effective wind speed – an experimental comparison, Wind Energ. Sci., 4, 421–438, https://doi.org/10.5194/wes-4-421-2019, 2019. a
IEC 61400-3-2: Wind energy generation systems – Part 3-2: Design requirements
for floating offshore wind turbines, 2019. a
fengguoFUAS: MSCA-LIKE/OpenFAST3.0_Lidarsim: Open-FAST3.0_Lidarsim (OpenFAST3.0_Lidarsim_v1), Zenodo
[code], https://doi.org/10.5281/zenodo.7594971, 2023a.
fengguoFUAS: MSCA-LIKE/4D-Mann-Turbulence-Generator: 4D-Mann-Turbulence-Generator (4D_MannTurbulence_v1),
Zenodo [code], https://doi.org/10.5281/zenodo.7594951, 2023b.
fengguoFUAS: MSCA-LIKE/Baseline-Lidar-assisted-Controller: Baseline-Lidar-assisted-Controller (Baseline-Lidar-assisted-Controllerv_1), Zenodo [code],
https://doi.org/10.5281/zenodo.7594961, 2023c.
Jonkman, J.: Influence of Control on the Pitch Damping of a Floating Wind
Turbine, 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, 7–10 January 2008,
https://doi.org/10.2514/6.2008-1306, 2008. a
Knight, J. M. and Obhrai, C.: The influence of an unstable turbulent wind
spectrum on the loads and motions on floating Offshore Wind Turbines, IOP
C. Ser. Mat. Sci., 700, 012005,
https://doi.org/10.1088/1757-899X/700/1/012005, 2019. a
Lemmer, F.: Low-order modeling, controller design and optimization of floating
offshore wind turbines, PhD thesis, Unversity of Stuttgart, 2018. a
Lemmer, F., Müller, K., Yu, W., Schlipf, D., and Cheng, P. W.: Optimization of
floating offshore wind turbine platforms with a self-tuning controller, in:
International Conference on Ocean, Offshore and Arctic Engineering,
Trondheim, Norway, 25–30 June 2017, https://doi.org/10.1115/omae2017-62038, 2017. a
Lemmer, F., Yu, W., Schlipf, D., and Cheng, P. W.: Robust gain scheduling
baseline controller for floating offshore wind turbines, Wind Energy, 23,
17–30, https://doi.org/10.1002/we.2408, 2020. a, b, c
Li, L., Gao, Z., and Moan, T.: Joint environmental data at five european
offshore sites for design of combined wind and wave energy devices, in:
International Conference on Offshore Mechanics and Arctic Engineering,
Nantes, France, 9–14 June 2013,
https://doi.org/10.1115/OMAE2013-10156, 2013. a, b
Lio, W. H., Meng, F., and Larsen, G. C.: On LiDAR-assisted wind turbine
retrofit control and fatigue load reductions, J. Phys. Conf.
Ser., 2265, 032072, https://doi.org/10.1088/1742-6596/2265/3/032072, 2022. a
Mann, J.: The spatial structure of neutral atmospheric surface-layer
turbulence, J. Fluid Mech., 273, 141–168, 1994. a
Mann, J.: Wind field simulation, Probabilist. Eng. Mech., 13,
269–282, 1998. a
Matsuishi, M. and Endo, T.: Fatigue of metals subjected to varying stress,
Japan Society of Mechanical Engineers, Fukuoka, Japan, 68, 37–40, 1968. a
Meng, F., Lio, W. H., and Larsen, G. C.: Wind turbine LIDAR-assisted control:
Power improvement, wind coherence and loads reduction, J. Phys. Conf. Ser., 2265, 022060, https://doi.org/10.1088/1742-6596/2265/2/022060,
2022. a
Mirzaei, M. and Mann, J.: Lidar configurations for wind turbine control,
J. Phys. Conf. Ser., 753, 032019,
https://doi.org/10.1088/1742-6596/753/3/032019, 2016. a
Nybø, A., Nielsen, F. G., Reuder, J., Churchfield, M. J., and Godvik, M.:
Evaluation of different wind fields for the investigation of the dynamic
response of offshore wind turbines, Wind Energy, 23, 1810–1830,
https://doi.org/10.1002/we.2518, 2020. a
Obukhov, A.: Turbulence in an atmosphere with a non-uniform temperature,
Bound.-Lay. Meteorol., 2, 7–29, https://doi.org/10.1007/BF00718085, 1971. a
Peña, A.: Østerild: A natural laboratory for atmospheric turbulence,
J. Renew. Sustain. Ener., 11, 063302,
https://doi.org/10.1063/1.5121486, 2019. a
Putri, R. M., Cheynet, E., Obhrai, C., and Jakobsen, J. B.: Turbulence in a coastal environment: the case of Vindeby, Wind Energ. Sci., 7, 1693–1710, https://doi.org/10.5194/wes-7-1693-2022, 2022. a
Rivera-Arreba, I., Wise, A. S., Hermile, M., Chow, F. K., and Bachynski-Polić,
E. E.: Effects of atmospheric stability on the structural response of a 12 MW
semisubmersible floating wind turbine, Wind Energy, 25, 1917–1937,
https://doi.org/10.1002/we.2775, 2022. a
Russell, A. J., Collu, M., McDonald, A., Thies, P. R., Mortimer, A., and
Quayle, A. R.: Review of LIDAR-assisted Control for Offshore Wind Turbine
Applications, J. Phys. Conf. Ser., 2362, 012035,
https://doi.org/10.1088/1742-6596/2362/1/012035, 2022. a
Sandner, F., Schlipf, D., Matha, D., and Cheng, P. W.: Integrated Optimization
of Floating Wind Turbine Systems, Ocean Renewable Energy of
International Conference on Offshore Mechanics and Arctic
Engineering, San Francisco, California, USA, 8–13 June 2014,
https://doi.org/10.1115/OMAE2014-24244, 2014. a
Schlipf, D., Simley, E., Lemmer, F., Pao, L., and Cheng, P. W.: Collective
pitch feedforward control of floating wind turbines using Lidar, Journal of
Ocean and Wind Energy, 2, 223–230, https://doi.org/10.17736/jowe.2015.arr04, 2015. a
Schlipf, D., Fürst, H., Raach, S., and Haizmann, F.: Systems engineering for
lidar-assisted control: a sequential approach, J. Phys. Conf. Ser., 1102, 012014,
https://doi.org/10.1088/1742-6596/1102/1/012014, 2018. a
Schlipf, D., Lemmer, F., and Raach, S.: Multi-Variable Feedforward Control for
Floating Wind Turbines Using Lidar, in: International Ocean and Polar
Engineering Conference, Shanghai, China, 11–16 October 2020, https://doi.org/10.18419/opus-11067, 2020. a, b
Simley, E. and Pao, L. Y.: Reducing LIDAR wind speed measurement error with
optimal filtering, in: Proceedings of the American Control Conference,
Washington, DC, USA, 17–19 June 2013, https://doi.org/10.1109/ACC.2013.6579906, 2013. a
Simley, E., Fürst, H., Haizmann, F., and Schlipf, D.: Optimizing lidars for
wind turbine control applications results from the IEA wind task 32
workshop, Remote Sens., 10, 863, https://doi.org/10.3390/rs10060863, 2018. a
Simley, E., Bortolotti, P., Scholbrock, A., Schlipf, D., and Dykes, K.: IEA
Wind Task 32 and Task 37: Optimizing Wind Turbines with Lidar-Assisted
Control Using Systems Engineering, J. Phys. Conf. Ser.,
1618, 042029, https://doi.org/10.1088/1742-6596/1618/4/042029, 2020. a, b
Somoano, M., Battistella, T., Rodríguez-Luis, A., Fernández-Ruano, S., and
Guanche, R.: Influence of turbulence models on the dynamic response of a
semi-submersible floating offshore wind platform, Ocean Eng., 237,
109629, https://doi.org/10.1016/j.oceaneng.2021.109629, 2021. a
Taylor, G. I.: The spectrum of turbulence, P. Roy. Soc. Lond. A Mat., 164, 476–490,
https://doi.org/10.1098/rspa.1938.0032, 1938. a
van der Veen, G., Couchman, I., and Bowyer, R.: Control of floating wind
turbines, in: Proceedings of the American Control Conference, Montreal,
Canada, 27–29 June 2012, https://doi.org/10.1109/ACC.2012.6315120, 2012. a
Ward, D., Collu, M., and Sumner, J.: Reducing Tower Fatigue through Blade Back
Twist and Active Pitch-to-Stall Control Strategy for a Semi-Submersible
Floating Offshore Wind Turbine, Energies, 12, 1897, https://doi.org/10.3390/en12101897, 2019. a
Wu, J. and Kim, M.-H.: Generic Upscaling Methodology of a Floating Offshore
Wind Turbine, Energies, 14, 8490, https://doi.org/10.3390/en14248490, 2021.
a
Short summary
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.
This paper assesses lidar-assisted collective pitch feedforward (LACPF) and multi-variable...
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