Articles | Volume 11, issue 6
https://doi.org/10.5194/wes-11-2053-2026
© Author(s) 2026. 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-11-2053-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Performance of multi-band MDE-based virtual sensing for estimating lifetime fatigue damage equivalent loads for the IEA 15 MW reference wind turbine
Department of Civil and of Mechanical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
COWI A/S, 2800 Kongens Lyngby, Denmark
Jennifer Marie Rinker
Department of Wind and Energy Systems, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Isaac Farreras Alcover
COWI A/S, 2800 Kongens Lyngby, Denmark
Jan Høgsberg
Department of Civil and of Mechanical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
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Esperanza Soto Sagredo, Søren Juhl Andersen, Ásta Hannesdóttir, and Jennifer Marie Rinker
Wind Energ. Sci., 11, 1705–1731, https://doi.org/10.5194/wes-11-1705-2026, https://doi.org/10.5194/wes-11-1705-2026, 2026
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Large wind turbines are highly sensitive to changing winds, yet current measurements miss important details. This study compares three methods to reconstruct the full wind field ahead of a turbine in real time using lidar data and simulations. The results show these approaches can capture detailed inflow structures, which could help turbines anticipate wind changes, improve control strategies, and reduce structural loads.
Alex Rybchuk, Henrik Asmuth, Armin Haghshenas, Ásta Hannesdóttir, Jan Friedrich, Jaime Liew, Jennifer M. Rinker, Daniel R. Houck, Regis Thedin, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-77, https://doi.org/10.5194/wes-2026-77, 2026
Preprint under review for WES
Short summary
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Wind turbine engineers use wind simulation tools as part of the design process. We conducted a benchmark study for these tools. We collected detailed wind data from two sets of environments (a field campaign and a research-grade simulation). We gave benchmark participants limited information about this data, and they used their wind simulation tools of choice to reconstruct the winds. We compared the output of the different simulation codes, identifying strengths and shortcomings.
Shadan Mozafari, Jennifer Marie Rinker, Paul Veers, and Katherine Dykes
Wind Energ. Sci., 11, 621–641, https://doi.org/10.5194/wes-11-621-2026, https://doi.org/10.5194/wes-11-621-2026, 2026
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The study showcases the added value of using structural response measurements in lifetime extension assessments within wind farms. In addition, it answers two of the common questions in different methods of assessment. First, it assesses the applicability of the Frandsen model for estimating conservative waked turbulence in the compact layout of wind farms. Second, it showcases probabilistic extrapolation of short- to mid-term data for long-term site-specific fatigue assessments.
Shadan Mozafari, Paul Veers, Jennifer Rinker, and Katherine Dykes
Wind Energ. Sci., 9, 799–820, https://doi.org/10.5194/wes-9-799-2024, https://doi.org/10.5194/wes-9-799-2024, 2024
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Turbulence is one of the main drivers of fatigue in wind turbines. There is some debate on how to model the turbulence in normal wind conditions in the design phase. To address such debates, we study the fatigue load distribution and reliability following different models of the International Electrotechnical Commission 61400-1 standard. The results show the lesser importance of load uncertainty due to turbulence distribution compared to the uncertainty of material resistance and Miner’s rule.
Cited articles
ASTM E1049-85: Standard practices for cycle counting in fatigue analysis, https://doi.org/10.1520/E1049-85R17, 2017. a
Augustyn, D., Pedersen, R. R., Tygesen, U. T., Ulriksen, M. D., and Sørensen, J. D.: Feasibility of modal expansion for virtual sensing in offshore wind jacket substructures, Marine Structures, 79, 1–17, https://doi.org/10.1016/j.marstruc.2021.103019, 2021. a, b, c
Baqersad, J., Niezrecki, C., and Avitabile, P.: Full-field dynamic strain prediction on a wind turbine using displacements of optical targets measured by stereophotogrammetry, Mech. Syst. Signal Pr., 62–63, 284–295, https://doi.org/10.1016/J.YMSSP.2015.03.021, 2015. a
Bilbao, J., Lourens, E.-M., Schulze, A., and Ziegler, L.: Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model, Data-Centric Engineering, 3, https://doi.org/10.1017/DCE.2022.38, 2022. a
de N Santos, F., D'Antuono, P., Robbelein, K., Noppe, N., Weijtjens, W., and Devriendt, C.: Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks, Renewable Energy, 205, 461–474, https://doi.org/10.1016/J.renene.2023.01.093, 2023. a
DSF/FprEN 1993-1-9: Draft no. M372165 – Eurocode 3: Design of steel structures – Part 1–9: Fatigue, 2024. a
Eftekhar Azam, S., Chatzi, E., and Papadimitriou, C.: A dual Kalman filter approach for state estimation via output-only acceleration measurements, Mech. Syst. Signal Pr., 60–61, 866–886, https://doi.org/10.1016/j.ymssp.2015.02.001, 2015. a
Ercan, T. and Papadimitriou, C.: Optimal sensor placement for reliable virtual sensing using modal expansion and information theory, Sensors, 21, https://doi.org/10.3390/s21103400, 2021. a
Fallais, D., Sastre Jurado, C., Weijtjens, W., and Devriendt, C.: Validation of a model-based dual-band modal decomposition and expansion approach for fatigue monitoring of offshore wind turbines, in: 11th European Workshop on Structural Health Monitoring, EWSHM 2024, vol. 29, NDT.net, https://doi.org/10.58286/29660, 2024. a, b
Faria, B. R., Dimitrov, N., Perez, V., Kolios, A., and Abrahamsen, A. B.: Virtual load sensors based on calibrated wind turbine strain sensors for damage accumulation estimation: A gap-filling technique, J. Phys. Conf. Ser., 3025, https://doi.org/10.1088/1742-6596/3025/1/012011, 2025. a
Gaertner, E., Rinker, J., Sethuraman, L., Anderson, B., Zahle, F., Barter, G., Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Sheilds, M., Allen, C., and Viselli, A.: IEA Wind TCP Task 37: Definition of the IEA Wind 15-Megawatt Offshore Reference Wind Turbine, Tech. rep., National Renewable Energy Laboratory, Golden CO, https://docs.nlr.gov/docs/fy20osti/75698.pdf (last access: 9 June 2026), 2020a. a, b, c, d, e
Gaertner, E., Rinker, J., Sethuraman, L., Anderson, B., Zahle, F., Barter, G., Nikhar, A., Fanzhong, M., Pietro, B., Witold, S., George, S., Roland, F., Henrik, B., Katherine, D., Matt, S., Christopher, A., and Anthony, V.: IEA-15-240-RWT Frequently Asked Questions (FAQ), https://github.com/IEAWindSystems/IEA-15-240-RWT/wiki/Frequently-Asked-Questions-(FAQ) (last access: 2 March 2025), 2020b. a
Gaertner, E., Rinker, J., Sethuraman, L., Anderson, B., Zahle, F., Barter, G., Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Sheilds, M., Allen, C., and Viselli, A.: IEAWindTask37/IEA-15-240-RWT: 15MW reference wind turbine repository developed in conjunction with IEA Wind. Version 1.1.6, GitHub [code], https://github.com/IEAWindTask37/IEA-15-240-RWT (last access: 23 February 2023), 2023. a, b
Ghoshal, A.: Colossal 20-MW wind turbine is the largest on the planet (for now), https://newatlas.com/energy/world-largest-offshore-wind-turbine-20-mw-mingyang/ (last access: 16 September 2025), 2024. a
Henkel, M., Häfele, J., Weijtjens, W., Devriendt, C., Gebhardt, C. G., and Rolfes, R.: Strain estimation for offshore wind turbines with jacket substructures using dual-band modal expansion, Marine Structures, 71, https://doi.org/10.1016/j.marstruc.2020.102731, 2020. a
Henkel, M., Weijtjens, W., and Devriendt, C.: Fatigue stress estimation for submerged and sub-soil welds of offshore wind turbines on monopiles using modal expansion, Energies, 14, https://doi.org/10.3390/en14227576, 2021. a
IEC: IEC 61400-1:2019, Wind energy generation systems – Part 1: Design requirements, 2019a. a
Iliopoulos, A., Shirzadeh, R., Weijtjens, W., Guillaume, P., Hemelrijck, D. V., and Devriendt, C.: A modal decomposition and expansion approach for prediction of dynamic responses on a monopile offshore wind turbine using a limited number of vibration sensors, Mech. Syst. Signal Pr., 68–69, 84–104, https://doi.org/10.1016/j.ymssp.2015.07.016, 2016. a
Iliopoulos, A. N., Devriendt, C., Iliopoulos, S. N., and Van Hemelrijck, D.: Continuous fatigue assessment of offshore wind turbines using a stress prediction technique, Health Monitoring of Structural and Biological Systems, 9064, 90640S, https://doi.org/10.1117/12.2045576, 2014. a
Krenk, S. and Høgsberg, J.: Statics and mechanics of structures, Springer, ISBN 978-94-007-6113-1, https://doi.org/10.1007/978-94-007-6113-1, 2013. a
Maes, K., Iliopoulos, A., Weijtjens, W., Devriendt, C., and Lombaert, G.: Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms, Mech. Syst. Signal Pr., 76–77, 592–611, https://doi.org/10.1016/j.ymssp.2016.01.004, 2016. a
Mehrjoo, A., Song, M., Moaveni, B., Papadimitriou, C., and Hines, E.: Optimal sensor placement for parameter estimation and virtual sensing of strains on an offshore wind turbine considering sensor installation cost, Mech. Syst. Signal Pr., 169, 108787, https://doi.org/10.1016/j.ymssp.2021.108787, 2022. a
Natarajan, A., Hansen, M. H., and Wang, S.: Design Load Basis for Offshore Wind turbines: DTU Wind Energy Report No. E-0133, DTU Department of Wind Energy, ISBN 978-87-93278-99-8, 2016. a
Noppe, N., Iliopoulos, A., Weijtjens, W., and Devriendt, C.: Full load estimation of an offshore wind turbine based on SCADA and accelerometer data, J. Phys. Conf. Ser., 753, 072025, https://doi.org/10.1088/1742-6596/753/7/072025, 2016. a, b
Reinhardt, T., Sastre Jurado, C., Weijtjens, W., and Devriendt, C.: On the influence of rotor nacelle assembly modelling on the computed eigenfrequencies of offshore wind turbines, J. Phys. Conf. Ser., 2767, https://doi.org/10.1088/1742-6596/2767/5/052034, 2024. a, b
Rinker, J., Gaertner, E., Zahle, F., Skrzypiński, W., Abbas, N., Bredmose, H., Barter, G., and Dykes, K.: Comparison of loads from HAWC2 and OpenFAST for the IEA Wind 15 MW Reference Wind Turbine, J. Phys. Conf. Ser., 1618, 052052, https://doi.org/10.1088/1742-6596/1618/5/052052, 2020. a
Salas, J.: Another turbine world record set – but not by China this time, https://newatlas.com/energy/siemens-gamesa-sg-dd-276-turbine/ (last access: 29 September 2025), 2025. a
Skafte, A., Kristoffersen, J., Vestermark, J., Tygesen, U. T., and Brincker, R.: Experimental study of strain prediction on wave induced structures using modal decomposition and quasi static Ritz vectors, Eng. Struct., 136, 261–276, https://doi.org/10.1016/j.engstruct.2017.01.014, 2017. a, b, c, d
Tarpø, M.: Stress Estimation of Offshore Structures, PhD thesis, Aahus University, ISBN 978-87-7507-491-4, https://doi.org/10.7146/aul.393, 2020. a, b, c
Toftekær, J. F., Vestermark, J. T., and Jepsen, M. S.: Uncertainty of Virtually Sensed Stress Ranges in Offshore Wind Support Structures, in: Proceedings of the ASME 2023 42nd International Conference on Ocean, Offshore and Arctic Engineering, V001T01A011, https://doi.org/10.1115/OMAE2023-101045, 2023. a, b, c, d, e, f, g, h, i
Vestas Wind Systems A/S: V236-15.0 MW™, https://www.vestas.com/en/energy-solutions/offshore-wind-turbines/V236-15MW, last access: 29 September 2025), 2026. a
Vettori, S., Di Lorenzo, E., Peeters, B., Luczak, M. M., and Chatzi, E.: An adaptive-noise Augmented Kalman Filter approach for input-state estimation in structural dynamics, Mech. Syst. Signal Pr., 184, 109654, https://doi.org/10.1016/j.ymssp.2022.109654, 2023. a
Zou, J., Lourens, E.-M., and Cicirello, A.: Virtual sensing of subsoil strain response in monopile-based offshore wind turbines via Gaussian process latent force models, Mech. Syst. Signal Pr., 200, 110488, https://doi.org/10.1016/J.YMSSP.2023.110488, 2023. a, b, c
Short summary
Offshore wind turbines are prone to fatigue caused by wind, wave, and operational loads, and lifetime extension may be enabled by monitoring stress histories. However, this is challenging because parts of the structure are sub‑sea and sub‑soil parts. Model‑based virtual sensing offers a solution, but current models simplify the rotor, which can lead to errors. This work addresses these errors and concludes that accuracy may be improved by including a flexible-rotor model and environmental variability.
Offshore wind turbines are prone to fatigue caused by wind, wave, and operational loads, and...
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