Articles | Volume 7, issue 1
https://doi.org/10.5194/wes-7-161-2022
© Author(s) 2022. 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-7-161-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
24 Jan 2022
Review article |
| 24 Jan 2022
| 24 Jan 2022
Damping identification of offshore wind turbines using operational modal analysis: a review
Aemilius A. W. van Vondelen
Delft Center for Systems and Control, Delft University of Technology, 2628CN Delft, the Netherlands
Sachin T. Navalkar
CORRESPONDING AUTHOR
Siemens Gamesa Renewable Energy, Prinses Beatrixlaan 800, 2595BN The Hague, the Netherlands
Alexandros Iliopoulos
Siemens Gamesa Renewable Energy, Prinses Beatrixlaan 800, 2595BN The Hague, the Netherlands
Daan C. van der Hoek
Delft Center for Systems and Control, Delft University of Technology, 2628CN Delft, the Netherlands
Jan-Willem van Wingerden
Delft Center for Systems and Control, Delft University of Technology, 2628CN Delft, the Netherlands
Related authors
Aemilius Adrianus Wilhelmus van Vondelen, Marion Coquelet, Sachin Tejwant Navalkar, and Jan-Willem van Wingerden
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-51, https://doi.org/10.5194/wes-2025-51, 2025
Revised manuscript accepted for WES
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Wind farms suffer energy losses due to wake effects between turbines. We present a new control strategy that synchronizes turbine wakes to enhance power output. By estimating and aligning the phase shifts of periodic wake structures using an advanced filtering method, downstream turbines recover more energy. Simulations show up to 10 % increased power at the third turbine. These results offer a promising path to improving wind farm efficiency while mixing wakes.
Unai Gutierrez Santiago, Aemilius A. W. van Vondelen, Alfredo Fernández Sisón, Henk Polinder, and Jan-Willem van Wingerden
Wind Energ. Sci., 10, 207–225, https://doi.org/10.5194/wes-10-207-2025, https://doi.org/10.5194/wes-10-207-2025, 2025
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Knowing the loads applied to wind turbine gearboxes throughout their service life is becoming increasingly important as maintaining reliability with higher torque density demands is proving to be challenging. Operational deflection shapes identified from fiber-optic strain measurements have enabled the estimation of input torque, improving the assessment of the consumed life. Tracking operational deflection shapes recursively over time can potentially be used as an indicator of fault detection.
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.
Marcus Becker, Maxime Lejeune, Philippe Chatelain, Dries Allaerts, Rafael Mudafort, and Jan-Willem van Wingerden
Wind Energ. Sci., 10, 1055–1075, https://doi.org/10.5194/wes-10-1055-2025, https://doi.org/10.5194/wes-10-1055-2025, 2025
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Established turbine wake models are steady-state. This paper presents an open-source dynamic wake modeling framework that complements established steady-state wake models with dynamics. It is advantageous over steady-state wake models to describe wind farm power and energy over shorter periods. The model enables researchers to investigate the effectiveness of wind farm flow control strategies. This leads to a better utilization of wind farms and allows them to be used to their fullest extent.
Amr Hegazy, Peter Naaijen, and Jan-Willem van Wingerden
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-68, https://doi.org/10.5194/wes-2025-68, 2025
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Floating wind turbines face stability issues when traditional onshore control methods are used, due to their motion at sea. This research reviews existing control strategies and introduces a new controller that improves stability without needing extra sensors. Simulations show it outperforms others in maintaining performance and reducing structural stress. The study highlights key trade-offs and the need for smarter, tailored control in offshore wind energy.
Aemilius Adrianus Wilhelmus van Vondelen, Marion Coquelet, Sachin Tejwant Navalkar, and Jan-Willem van Wingerden
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-51, https://doi.org/10.5194/wes-2025-51, 2025
Revised manuscript accepted for WES
Short summary
Short summary
Wind farms suffer energy losses due to wake effects between turbines. We present a new control strategy that synchronizes turbine wakes to enhance power output. By estimating and aligning the phase shifts of periodic wake structures using an advanced filtering method, downstream turbines recover more energy. Simulations show up to 10 % increased power at the third turbine. These results offer a promising path to improving wind farm efficiency while mixing wakes.
Unai Gutierrez Santiago, Aemilius A. W. van Vondelen, Alfredo Fernández Sisón, Henk Polinder, and Jan-Willem van Wingerden
Wind Energ. Sci., 10, 207–225, https://doi.org/10.5194/wes-10-207-2025, https://doi.org/10.5194/wes-10-207-2025, 2025
Short summary
Short summary
Knowing the loads applied to wind turbine gearboxes throughout their service life is becoming increasingly important as maintaining reliability with higher torque density demands is proving to be challenging. Operational deflection shapes identified from fiber-optic strain measurements have enabled the estimation of input torque, improving the assessment of the consumed life. Tracking operational deflection shapes recursively over time can potentially be used as an indicator of fault detection.
Claudia Muscari, Paolo Schito, Axelle Viré, Alberto Zasso, and Jan-Willem van Wingerden
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-149, https://doi.org/10.5194/wes-2024-149, 2025
Publication in WES not foreseen
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This paper presents the findings of a study aimed at describing the flow system downstream of a wind turbine operated with a novel control technology. Results from heavy high-fidelity simulations are used to obtain a low-fidelity model that is quick enough to be used for the optimization of such technologies. Additionally, we were able to retrieve an improved understanding of the physics of such systems under different inflow conditions.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024, https://doi.org/10.5194/wes-9-2171-2024, 2024
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Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
Matteo Baricchio, Pieter M. O. Gebraad, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 2113–2132, https://doi.org/10.5194/wes-9-2113-2024, https://doi.org/10.5194/wes-9-2113-2024, 2024
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Wake steering can be integrated into wind farm layout optimization through a co-design approach. This study estimates the potential of this method for a wide range of realistic conditions, adopting a tailored genetic algorithm and novel geometric yaw relations. A gain in the annual energy yield between 0.3 % and 0.4 % is obtained for a 16-tubrine farm, and a multi-objective implementation is used to limit loss in the case that wake steering is not used during farm operation.
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
Short summary
Short summary
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.
Maarten J. van den Broek, Marcus Becker, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 721–740, https://doi.org/10.5194/wes-9-721-2024, https://doi.org/10.5194/wes-9-721-2024, 2024
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Wind turbine wakes negatively affect wind farm performance as they impinge on downstream rotors. Wake steering reduces these losses by redirecting wakes using yaw misalignment of the upstream rotor. We develop a novel control strategy based on model predictions to implement wake steering under time-varying conditions. The controller is tested in a high-fidelity simulation environment and improves wind farm power output compared to a state-of-the-art reference controller.
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.
Maarten J. van den Broek, Delphine De Tavernier, Paul Hulsman, Daan van der Hoek, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1909–1925, https://doi.org/10.5194/wes-8-1909-2023, https://doi.org/10.5194/wes-8-1909-2023, 2023
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As wind turbines produce power, they leave behind wakes of slow-moving air. We analyse three different models to predict the effects of these wakes on downstream wind turbines. The models are validated with experimental data from wind tunnel studies for steady and time-varying conditions. We demonstrate that the models are suitable for optimally controlling wind turbines to improve power production in large wind farms.
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.
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
Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, https://doi.org/10.5194/wes-7-2271-2022, 2022
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We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
Marcus Becker, Bastian Ritter, Bart Doekemeijer, Daan van der Hoek, Ulrich Konigorski, Dries Allaerts, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2163–2179, https://doi.org/10.5194/wes-7-2163-2022, https://doi.org/10.5194/wes-7-2163-2022, 2022
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In this paper we present a revised dynamic control-oriented wind farm model. The model can simulate turbine wake behaviour in heterogeneous and changing wind conditions at a very low computational cost. It utilizes a three-dimensional turbine wake model which also allows capturing vertical wind speed differences. The model could be used to maximise the power generation of with farms, even during events like a wind direction change. It is publicly available and open for further development.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
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The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
Daan van der Hoek, Joeri Frederik, Ming Huang, Fulvio Scarano, Carlos Simao Ferreira, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 1305–1320, https://doi.org/10.5194/wes-7-1305-2022, https://doi.org/10.5194/wes-7-1305-2022, 2022
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The paper presents a wind tunnel experiment where dynamic induction control was implemented on a small-scale turbine. By periodically changing the pitch angle of the blades, the low-velocity turbine wake is perturbed, and hence it recovers at a faster rate. Small particles were released in the flow and subsequently recorded with a set of high-speed cameras. This allowed us to reconstruct the flow behind the turbine and investigate the effect of dynamic induction control on the wake.
Yichao Liu, Riccardo Ferrari, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 523–537, https://doi.org/10.5194/wes-7-523-2022, https://doi.org/10.5194/wes-7-523-2022, 2022
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The objective of the paper is to develop a data-driven output-constrained individual pitch control approach, which will not only mitigate the blade loads but also reduce the pitch activities. This is achieved by only reducing the blade loads violating a user-defined bound, which leads to an economically viable load control strategy. The proposed control strategy shows promising results of load reduction in the wake-rotor overlapping and turbulent sheared wind conditions.
Unai Gutierrez Santiago, Alfredo Fernández Sisón, Henk Polinder, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 505–521, https://doi.org/10.5194/wes-7-505-2022, https://doi.org/10.5194/wes-7-505-2022, 2022
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The gearbox is one of the main contributors to the overall cost of wind energy, and it is acknowledged that we still do not fully understand its loading. The study presented in this paper develops a new alternative method to measure input rotor torque in wind turbine gearboxes, overcoming the drawbacks related to measuring on a rotating shaft. The method presented in this paper could make measuring gearbox torque more cost-effective, which would facilitate its adoption in serial wind turbines.
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.
Cited articles
Alberici, S., Boeve, S., Van Breefoort, P., Deng, Y., Förster, S., Gardiner, A., Van Gastel, V., Grave, K., Groenenberg, H., De Jager, D., Klaassen, E., Pouwels, W., Smith, M., De Visser, E., Winkel, T., and Wouters, K.: Subsidies and costs of EU energy: Final report, technical report, European Commission, DESNL14583, available at: https://ec.europa.eu/energy/sites/ener/files/documents/ECOFYS 2014 Subsidies and costs of EU energy_11_Nov.pdf (last access: 11 May 2021), 2014. a
American Petroleum Institute: Recommended Practice for Planning, Design and Constructing
Fixed Offshore Platforms – Working Stress Design American Petroleum
Institute, 21st edition, available at: https://www.api.org/~/media/files/publications/whats new/2a-wsd_e22 pa.pdf (last access: 21 May 2021), 2000. a
Araújo, I. G. and Laier, J. E.: Operational modal analysis using SVD of power spectral density transmissibility matrices, Mech. Syst. Signal Pr., 46, 129–145, 2014. a
Araújo, I. G., Sánchez, J. A. G., and Andersen, P.: Modal parameter identification based on combining transmissibility functions and blind source separation techniques, Mech. Syst. Signal Pr., 105, 276–293, 2018. a
Bajric, A., Brincker, R., and Thöns, S.: Evaluation of damping estimates in the presence of closely spaced modes using operational modal analysis techniques, Proceedings of the 6th International Operational Modal Analysis Conference, Gijon, Spain, 12–14 May 2015, available at: https://orbit.dtu.dk/en/publications/evaluation-of-damping-estimates-in-the-presence-of-closely-spaced
(last access: 21 May 2021), 2015. a
Bendat, J. S. and Piersol, A. G.: Engineering applications of correlation and spectral analysis, Wiley-Interscience Publication, ISBN 13: 9780471058878, 1980. a
Blanco, M. I.: The economics of wind energy, Adv. Mater. Res.-Switz., 13, 1372–1382, 2009. a
Braun, S.: The synchronous (time-domain) average revisited, Mech. Syst. Signal Pr., 25, 1087–1102, 2011. a
Brincker, R., Andersen, P., and Møller, N.: An Indicator for Separation of Structural and Harmonic Modes in
Output-Only Modal Testing, in: Proceedings of the European COST F3 Conference on
System Identification & Structural Health Monitoring, edited by: Güemes, J. A., 6–9 June, 2000, Universidad Politécnica de Madrid, Spain
Universidad Politécnica de Madrid, 265–272, 2000a. a, b
Brincker, R., Zhang, L., and Andersen, P.: Modal identification from ambient responses using frequency-domain decomposition, Proceedings of the 18th International Modal Analysis Conference, San Antonio, Texas, USA, 7–10 February 2000, 625–630, available at: https://vbn.aau.dk/en/publications/modal-identification-from-ambient-responses-using-frequency-domai-2
(last access: 21 May 2021), 2000. a, b, c
Devriendt, C. and Guillaume, P.: Identification of modal parameters from transmissibility measurements, J. Sound Vib., 134, 343–356, 2008. a
Devriendt, C., De Sitter, G., and Guillaume, P.: An operational modal analysis approach based on parametrically identified multivariable transmissibilities, Mech. Syst. Signal Pr., 24, 1250–1259, 2010. a
Devriendt, C., Magalhães, F., Weijtjens, W., De Sitter, G., Cunha, Á., and Guillaume, P.: Structural health monitoring of offshore wind turbines using automated operational modal analysis, Struct. Health Monit., 13, 644–659, 2014. a
DNV: Design of offshore wind turbine structures Det Norske Veritas,
DNV-OS-J101, available at: https://rules.dnv.com/docs/pdf/dnvpm/codes/docs/2004-06/Os-J101.pdf (last access: 21 May 2021), 2004. a
Dong, X., Lian, J., Yang, M., and Wang, H.: Operational modal identification of offshore wind turbine structure based on modified stochastic subspace identification method considering harmonic interference, J. Renew. Sustain. Ener., 6, 033128, https://doi.org/10.1063/1.4881876, 2014. a, b, c
European Commission: “Energy roadmap 2050” (COM(2011), 885 final of 15 December 2011, available at: https://ec.europa.eu/energy/sites/ener/files/documents/roadmap2050_ia_20120430_en_0.pdf (last access: 21 May 2021), 2011. a
Gantasala, S., Luneno, J.-C., and Aidanpää, J.-O.: Identification of ice mass accumulated on wind turbine blades using its natural frequencies, Wind Engineering, 42, 66–84, 2018. a
Gasparis, G.: A benchmark study on operational modal analysis system identification algorithms for operating offshore wind turbines, MS thesis, Delft University of Technology, available at: http://resolver.tudelft.nl/uuid:75c8bf27-9215-4ab3-8e65-57dca591a007 (last access: 21 May 2021), 2019. a
Germanischer Lloyd: Rules & Guidelines 2000: IV Non-marine
Technology – Regulations for the Certification of (Offshore) Wind Energy
Conversion Systems, available at: https://rules.dnv.com/docs/pdf/xtra/gl-class/GLRules_2010.zip?_ga=2.3200176.1839843236.1642754715-687497769.1638862854 (last access: 21 May 2021), 2000. a
Greś, S., Döhler M., Andersen, P., and Mevel, L.: Kalman filter-based subspace identification for operational modal analysis under unmeasured periodic excitation, Mech. Syst. Signal Pr., 146, 106996, https://doi.org/10.1016/j.ymssp.2020.106996, 2021. a, b, c, d
Global Wind Energy Council (GWEC): Global offshore wind report 2020, available at: https://gwec.net/global-offshore-wind-report-2020/#download-report (last access: 21 May 2021), 2020. a
Ibrahim, S. R.: A time domain vibration test technique, PhD Thesis, University of Calgary, https://doi.org/10.11575/PRISM/23317, 1973. a, b
ISO 19902 DIS: Petroleum and natural gas industries specific
requirements for fixed offshore structures Issued 2004-09-30 by the International
Standards Organization, 2004. a
Jacobsen, N.-J., Andersen, P., and Brincker R.: Using EFDD as a robust technique for deterministic excitation in operational modal analysis, Proceedings of the 2nd International Modal Analysis Conference, Copenhagen, Denmark, 30 April–2 May 2007, Vol. 1, 193–200, ISBN 8791606136, 2007. a, b, c, d, e
Kihm, F., Langelier, A., and Munson, K.: Influence of the modal damping on the estimated fatigue life, Procedia Engineer., 213, 270–281, 2018. a
Koukoura, C., Natarajan, A., and Vesth, A.: Identification of support structure damping of a full scale offshore wind turbine in normal operation, Renew. Energ., 81, 882–895, 2015. a
Kramers, H. and Van der Valk, P., and Wingerden, J. W.: Statistical Evaluation of the Identified Structural Parameters of an Idling Offshore Wind Turbine,
J. Phys.-Conf. Ser., 753, 052006, https://doi.org/10.1088/1742-6596/753/5/052006, 2016. a, b
Ljung, L.: System Identification: Theory for the User, Prentice Hall information and sytem sciences series, edited by: Jordan, G., Engelwood Cliffs, NJ, ISBN 0-13-881640-9, 1987. a
Maia, N. M., Silva, J. M., and Ribeiro, A. M.: The transmissibility concept in multi-degree-of-freedom systems, Mech. Syst. Signal Pr., 15, 129–137, 2001. a
Majji, M., Juang, J.-N., and Junkins, J. L.: Time-varying eigenrealisation algorithm, J. Guid. Control Dynam., 33, 13–28, 2010. a
Malekjafarian, A., Brincker, R., Ashory, M. R., and Khatibi, M. M.: Identification of closely spaced modes using the Ibrahim time domain method, Proceedings of the 4th International Operational Modal Analysis Conference, Istanbul, Turkey, 9–11 May 2011, 120–126, ISBN 978-163266853-0, 2010. a
Manzato, S., White, J. R., LeBlanc, B., Peeters, B., and Janssens, K.: Advanced identification techniques for operational wind turbine data, C. Proc. Soc. Exp. Mech., 7, 195–209, 2014. a
Martinez-Luengo, M., Kolios, A., and Wang, L.: Structural health monitoring of offshore wind turbines: a review through the statistical pattern recognition paradigm, Renew. Sust. Energ. Rev., 64, 91–105, 2016. a
Mohanty, P. and Rixen, D. J.: Modified ERA method for operational modal analysis in the presence of harmonic excitation, Mech. Syst. Signal Pr., 20, 114–130, 2006. a
Nicholson, J. C., Arora, J. S., Goyal, D., and Tinjum, J. M.: Multi-objective structural optimisation of wind turbine tower and foundation systems using Isight: a process automation and design exploration software, 10th World Congress on Structural and Multidisciplinary Optimisation, Orlando, Florida, USA, 19–24 May 2013, available at: https://mae.ufl.edu/mdo/Papers/5423.pdf (last access: 21 May 2021), 2013. a
Peeters, B. and De Roeck, G.: Reference-based stochastic subspace identification for output-only modal analysis, Mech. Syst. Signal Pr., 13, 855–878, 1999. a
Peeters, B., Van der Auweraer, H., Guillaume, P., and Leuridan, J.: The PolyMAX frequency-domain method: a new standard for modal parameter estimation?, Shock Vib., 11, 395–409, 2004. a
Randall, R. B. and Hee, J.: Cepstrum analysis, Wireless World, 88, IPC Business Press Ltd., available at: https://worldradiohistory.com/UK/Wireless-World/80s/Wireless-World-1982-02.pdf (last access: 21 May 2021), 77–80, 1982. a
Randall, R. B., Peeters, B., Antoni, J., and Manzato, S.: New cepstral methods of signal pre-processing for operational modal analysis, Proceedings of the 25th International Conference on noise and Vibration Engineering, Leuven, Belgium, 17–19 September 2012, available at: http://past.isma-isaac.be/downloads/isma2012/papers/isma2012_0865.pdf v, 2012. a, b, c, d, e
Rainieri, C. and Fabbrocino, G.: Operational modal analysis of civil engineering structures, Springer, https://doi.org/10.1007/978-1-4939-0767-0, 2014. a, b, c, d
Rezaei, R., Fromme, P., and Duffour, P.: Fatigue life sensitivity of monopile-supported offshore wind turbines to damping, Renew. Energ., 123, 450–459, 2018. a
Rezaei, M. M., Behzad, M., Moradi, H., and Haddadpour, H.: Modal-based damage identification for the nonlinear model of modern wind turbine blade, Renew. Energ., 94, 391–409, 2016. a
Reynders, E., Pintelon, R., and De Roeck, G.: Uncertainty bounds on modal parameters obtained from stochastic subspace identification, Mech. Syst. Signal Pr., 22, 948–969, 2008. a
Reynders, E., Houbrechts, J., and De Roeck, G.: Fully automated (operational) modal analysis, Mech. Syst. Signal Pr., 29, 228–250, 2012. a
Reynders, E., Maes, K., Lombaert, G., and De Roeck, G.: Uncertainty quantification in operational modal analysis with stochastic subspace identification: validation and applications, Mech. Syst. Signal Pr., 66–67, 13–30, 2016. a
Van der Valk, P. L. C.: Coupled simulations of wind turbines and offshore support structures: strategies based on the dynamic substructuring paradigm, PhD thesis, Delft University of Technology, https://doi.org/10.4233/uuid:ac619319-9eae-443d-8b94-d0246f80ffdb, 2014. a
Van Overschee, P. and De Moor, B.: Subspace algorithms for the identification of combined deterministic-stochastic systems, Automatica, 30, 75–93, 1994. a
Van Overschee, P. and De Moor, B.: Subspace algorithms for the stochastic identification problem, Proceedings of the 30th IEEE Conference on Decision and Control, 2, Brighton, UK, 11–13 December 1991, 1321–1326, https://doi.org/10.1109/CDC.1991.261604, 1991. a, b, c
Van Vondelen, A. A. W., Iliopoulos, A., Navalkar, S. T., Van der Hoek, D. C., Van Wingerden, J. W.: Damping Identification of an Operational Offshore Wind Turbine using Enhanced Kalman filter-based Subspace Identification,
arXiv [preprint], arXiv:2201.07531, 2022. a
Versteijlen, W. G., Metrikine, A. V., Hoving, J. S., Smid, E., and De Vries, W. E.: Estimation of the vibration decrement of an offshore wind turbine support structure caused by its interaction with soil, Proceedings of EWEA Offshore 2011, Amsterdam, the Netherlands, European Wind Energy Association, 29 November–1 December, available at: http://resolver.tudelft.nl/uuid:1b9b84a8-148e-4250-82cb-306435dea7ad (last access: 21 May 2022), 10 pp., 2011.
a, b
Yan, W.-J., Zhao, M.-Y., Sun, Q., and Ren, W.-X.: Transmissibility-based system identification for structural health monitoring: fundamentals, approaches, and applications, Mech. Syst. Signal Pr., 117, 453–482, 2019. a
Short summary
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.
The damping of an offshore wind turbine is a difficult physical quantity to predict, although it...
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