Articles | Volume 11, issue 2
https://doi.org/10.5194/wes-11-373-2026
© 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-11-373-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Feb 2026
Research article |
| 06 Feb 2026
| 06 Feb 2026
A wind turbine digital shadow for complex inflow conditions
Hadi Hoghooghi
Wind Energy Institute, Technical University of Munich, 85748 Garching bei München, Germany
Wind Energy Institute, Technical University of Munich, 85748 Garching bei München, Germany
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Nicola Bodini, Patrick Moriarty, Regis Thedin, Paula Doubrawa, Cristina Archer, Myra Blaylock, Carlo Bottasso, Bruno Carmo, Lawrence Cheung, Camille Dubreuil, Rogier Floors, Thomas Herges, Daniel Houck, Ali Kanjari, Colleen M. Kaul, Christopher Kelley, Ru LI, Julie K. Lundquist, Desirae Major, Anh Kiet Nguyen, Mike Optis, Luan R. C. Parada, Alfredo Peña, Julian Quick, David Ricarte, William C. Radünz, Raj K. Rai, Oscar Garcia Santiago, Jonas Schulte, Knut S. Seim, M. Paul van der Laan, Kisorthman Vimalakanthan, and Adam Wise
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-34, https://doi.org/10.5194/wes-2026-34, 2026
Preprint under review for WES
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Predicting wind farm energy production is challenging because wind patterns are complex. We tested 16 different models against real data from a major field experiment to see which worked best. Surprisingly, the most expensive and detailed models were not always more accurate than simpler ones. We found that feeding models better weather data was the most effective way to improve accuracy. These results help the industry choose the right tools for designing more efficient wind farms.
Andreas Vad, Adrien Guilloré, Abhinav Anand, Vasilis Pettas, Anik H. Shah, Ion Lizarraga-Saenz, Maria Aparicio-Sanchez, Irene Eguinoa, Nicolau Conti Gost, Iasonas Tsaklis, Ariane Frère, Koen W. Hermans, Joseph K. Kamau, Nikolay Dimitrov, Tuhfe Göçmen, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-45, https://doi.org/10.5194/wes-2026-45, 2026
Preprint under review for WES
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A modular, computationally efficient framework for wind‑farm response modeling is presented. It combines an engineering wake model with surrogate models trained on extensive aeroelastic simulations generated using a novel method for synthetic waked and clean inflows. The wind‑farm‑agnostic framework supports multiple turbine types and layouts, enabling accurate, low‑cost predictions for design, operation, and control.
Carlo L. Bottasso, Sandrine Aubrun, Nicolaos A. Cutululis, Julia Gottschall, Athanasios Kolios, Jakob Mann, and Paul Veers
Wind Energ. Sci., 11, 347–348, https://doi.org/10.5194/wes-11-347-2026, https://doi.org/10.5194/wes-11-347-2026, 2026
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This editorial celebrates the 10th anniversary of Wind Energy Science, reflecting on a decade of rapid scientific progress and the journal’s role in advancing fundamental, interdisciplinary research. It highlights key developments in wind energy, the importance of open science and academia–industry collaboration, and emerging challenges such as data sharing and artificial intelligence. Above all, it honors the research community that has shaped the journal and looks ahead to the next decade.
Simone Tamaro, Davide Bortolin, Filippo Campagnolo, Franz V. Muehle, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-254, https://doi.org/10.5194/wes-2025-254, 2025
Revised manuscript under review for WES
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This work presents the scaled experimental validation of an active power control (APC) algorithm that improves wind-farm power-tracking accuracy during turbulent wind lulls. Tests in a large low-blockage wind tunnel, including dynamic wind-direction changes, show that the method outperforms three reference APC strategies, especially under high power demand, while keeping structural fatigue low.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 10, 2705–2728, https://doi.org/10.5194/wes-10-2705-2025, https://doi.org/10.5194/wes-10-2705-2025, 2025
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We proposed a new method for active power control that uniquely combines induction control with wake steering to maximize power tracking margins. Our methodology results in significantly improved robustness against wind fluctuations and fatigue loading when compared to the state of the art.
Abhinav Anand and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-101, https://doi.org/10.5194/wes-2025-101, 2025
Revised manuscript under review for WES
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We formulate a controller for wind turbines that has three main characteristics. First, it optimizes profit by balancing revenue from power generation with cost. Second, cost includes the effects of cyclic fatigue that, departing from most of the existing literature on control, is rigorously accounted for by an exact cycle counting on receding horizons. Third, it uses a model capable of learning and improving its performance based on measured or synthetic data.
Andre Thommessen, Abhinav Anand, Christoph M. Hackl, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-72, https://doi.org/10.5194/wes-2025-72, 2025
Revised manuscript accepted for WES
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We present a method to forecast inertia that accounts for wake effects in a wind farm. The approach is based on mapping forecasted site conditions to each single wind turbine in the farm through a wake model. The resulting inflow conditions are used to predict the inertia that the wind farm can provide to the grid, taking the wind turbine control strategies and operational limits into account.
Abhinav Anand, Robert Braunbehrens, Adrien Guilloré, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-67, https://doi.org/10.5194/wes-2025-67, 2025
Revised manuscript under review for WES
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We present a new method for wind farm control, based on the optimization of an economic cost function that accounts for revenue from power production and cost due to operation and maintenance. The new formulation also includes constraints to ensure a desired lifetime duration. The application to relevant scenarios shows consistently improved profit when compared to alternative formulations from the recent literature.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1547–1575, https://doi.org/10.5194/wes-9-1547-2024, https://doi.org/10.5194/wes-9-1547-2024, 2024
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We develop a new simple model to predict power losses incurred by a wind turbine when it yaws out of the wind. The model reveals the effects of a number of rotor design parameters and how the turbine is governed when it yaws. The model exhibits an excellent agreement with large eddy simulations and wind tunnel measurements. We showcase the capabilities of the model by deriving the power-optimal yaw strategy for a single turbine and for a cluster of wake-interacting turbines.
Marta Bertelè, Paul J. Meyer, Carlo R. Sucameli, Johannes Fricke, Anna Wegner, Julia Gottschall, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1419–1429, https://doi.org/10.5194/wes-9-1419-2024, https://doi.org/10.5194/wes-9-1419-2024, 2024
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A neural observer is used to estimate shear and veer from the operational data of a large wind turbine equipped with blade load sensors. Comparison with independent measurements from a nearby met mast and profiling lidar demonstrate the ability of the
rotor as a sensorconcept to provide high-quality estimates of these inflow quantities based simply on already available standard operational data.
Jenna Iori, Carlo Luigi Bottasso, and Michael Kenneth McWilliam
Wind Energ. Sci., 9, 1289–1304, https://doi.org/10.5194/wes-9-1289-2024, https://doi.org/10.5194/wes-9-1289-2024, 2024
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The controller of a wind turbine has an important role in regulating power production and avoiding structural failure. However, it is often designed after the rest of the turbine, and thus its potential is not fully exploited. An alternative is to design the structure and the controller simultaneously. This work develops a method to identify if a given turbine design can benefit from this new simultaneous design process. For example, a higher and cheaper turbine tower can be built this way.
Paul Veers, Carlo L. Bottasso, Lance Manuel, Jonathan Naughton, Lucy Pao, Joshua Paquette, Amy Robertson, Michael Robinson, Shreyas Ananthan, Thanasis Barlas, Alessandro Bianchini, Henrik Bredmose, Sergio González Horcas, Jonathan Keller, Helge Aagaard Madsen, James Manwell, Patrick Moriarty, Stephen Nolet, and Jennifer Rinker
Wind Energ. Sci., 8, 1071–1131, https://doi.org/10.5194/wes-8-1071-2023, https://doi.org/10.5194/wes-8-1071-2023, 2023
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Critical unknowns in the design, manufacturing, and operation of future wind turbine and wind plant systems are articulated, and key research activities are recommended.
Helena Canet, Adrien Guilloré, and Carlo L. Bottasso
Wind Energ. Sci., 8, 1029–1047, https://doi.org/10.5194/wes-8-1029-2023, https://doi.org/10.5194/wes-8-1029-2023, 2023
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We propose a new approach to design that aims at optimal trade-offs between economic and environmental goals. New environmental metrics are defined, which quantify impacts in terms of CO2-equivalent emissions produced by the turbine over its entire life cycle. For some typical onshore installations in Germany, results indicate that a 1 % increase in the cost of energy can buy about a 5 % decrease in environmental impacts: a small loss for the individual can lead to larger gains for society.
Robert Braunbehrens, Andreas Vad, and Carlo L. Bottasso
Wind Energ. Sci., 8, 691–723, https://doi.org/10.5194/wes-8-691-2023, https://doi.org/10.5194/wes-8-691-2023, 2023
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The paper presents a new method in which wind turbines in a wind farm act as local sensors, in this way detecting the flow that develops within the power plant. Through this technique, we are able to identify effects on the flow generated by the plant itself and by the orography of the terrain. The new method not only delivers a flow model of much improved quality but can also help in understanding phenomena that drive the farm performance.
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).
Emmanouil M. Nanos, Carlo L. Bottasso, Simone Tamaro, Dimitris I. Manolas, and Vasilis A. Riziotis
Wind Energ. Sci., 7, 1641–1660, https://doi.org/10.5194/wes-7-1641-2022, https://doi.org/10.5194/wes-7-1641-2022, 2022
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A novel way of wind farm control is presented where the wake is deflected vertically to reduce interactions with downstream turbines. This is achieved by moving ballast in a floating offshore platform in order to pitch the support structure and thereby tilt the wind turbine rotor disk. The study considers the effects of this new form of wake control on the aerodynamics of the steering and wake-affected turbines, on the structure, and on the ballast motion system.
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.
Emmanouil M. Nanos, Carlo L. Bottasso, Filippo Campagnolo, Franz Mühle, Stefano Letizia, G. Valerio Iungo, and Mario A. Rotea
Wind Energ. Sci., 7, 1263–1287, https://doi.org/10.5194/wes-7-1263-2022, https://doi.org/10.5194/wes-7-1263-2022, 2022
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The paper describes the design of a scaled wind turbine in detail, for studying wakes and wake control applications in the known, controllable and repeatable conditions of a wind tunnel. The scaled model is characterized by conducting experiments in two wind tunnels, in different conditions, using different measurement equipment. Results are also compared to predictions obtained with models of various fidelity. The analysis indicates that the model fully satisfies the initial requirements.
Helena Canet, Stefan Loew, and Carlo L. Bottasso
Wind Energ. Sci., 6, 1325–1340, https://doi.org/10.5194/wes-6-1325-2021, https://doi.org/10.5194/wes-6-1325-2021, 2021
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Lidar-assisted control (LAC) is used to redesign the rotor and tower of three turbines, differing in terms of wind class, size, and power rating. The load reductions enabled by LAC are used to save
mass, increase hub height, or extend lifetime. The first two strategies yield reductions in the cost of energy only for the tower of the largest machine, while more interesting benefits are obtained for lifetime extension.
Chengyu Wang, Filippo Campagnolo, Helena Canet, Daniel J. Barreiro, and Carlo L. Bottasso
Wind Energ. Sci., 6, 961–981, https://doi.org/10.5194/wes-6-961-2021, https://doi.org/10.5194/wes-6-961-2021, 2021
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This paper quantifies the fidelity of the wakes generated by a small (1 m diameter) scaled wind turbine model operated in a large boundary layer wind tunnel. A detailed scaling analysis accompanied by large-eddy simulations shows that these wakes are very realistic scaled versions of the ones generated by the parent full-scale wind turbine in the field.
Marta Bertelè, Carlo L. Bottasso, and Johannes Schreiber
Wind Energ. Sci., 6, 759–775, https://doi.org/10.5194/wes-6-759-2021, https://doi.org/10.5194/wes-6-759-2021, 2021
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A previously published wind sensing method is applied to an experimental dataset obtained from a 3.5 MW turbine and a nearby hub-tall met mast. The method uses blade load harmonics to estimate rotor-equivalent shears and wind directions at the rotor disk. Results indicate the good quality of the estimated shear, both in terms of 10 min averages and of resolved time histories, and a reasonable accuracy in the estimation of the yaw misalignment.
Helena Canet, Pietro Bortolotti, and Carlo L. Bottasso
Wind Energ. Sci., 6, 601–626, https://doi.org/10.5194/wes-6-601-2021, https://doi.org/10.5194/wes-6-601-2021, 2021
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The paper analyzes in detail the problem of scaling, considering both the steady-state and transient response cases, including the effects of aerodynamics, elasticity, inertia, gravity, and actuation. After a general theoretical analysis of the problem, the article considers two alternative ways of designing a scaled rotor. The two methods are then applied to the scaling of a 10 MW turbine of 180 m in diameter down to three different sizes (54, 27, and 2.8 m).
Cited articles
Abdallah, I., Tatsis, K., and Chatzi, E.: Fatigue assessment of a wind turbine blade when output from multiple aero-elastic simulators are available, Procedia Engineering, 199, 3170–3175, https://doi.org/10.1016/j.proeng.2017.09.509, x International Conference on Structural Dynamics, EURODYN 2017, 2017. a, b, c
Anand, A. and Bottasso, C. L.: Reducing Plant-Model Mismatch for Economic Model Predictive Control of Wind Turbine Fatigue by a Data-Driven Approach, in: 2023 American Control Conference (ACC), 14731479, https://doi.org/10.23919/ACC55779.2023.10156501, 2023. a, b, c
Bangalore, P., Letzgus, S., Karlsson, D., and Patriksson, M.: An artificial neural network-based condition monitoring method for wind turbines, with application to the monitoring of the gearbox, Wind Energy, 20, 1421–1438, https://doi.org/10.1002/we.2102, 2017. a, b
Bernhammer, L. O., van Kuik, G. A., and De Breuker, R.: Fatigue and extreme load reduction of wind turbine components using smart rotors, Journal of Wind Engineering and Industrial Aerodynamics, 154, 84–95, https://doi.org/10.1016/j.jweia.2016.04.001, 2016. a
Bertelè, M., Bottasso, C. L., and Schreiber, J.: Wind inflow observation from load harmonics: initial steps towards a field validation, Wind Energy Science, 6, 759–775, https://doi.org/10.5194/wes-6-759-2021, 2021. a, b
Bertelè, M., Meyer, P. J., Sucameli, C. R., Fricke, J., Wegner, A., Gottschall, J., and Bottasso, C. L.: The rotor as a sensor – Observing shear and veer from the operational data of a large wind turbine, Wind Energy Science, 9, 1419–1429, https://doi.org/10.5194/wes-9-1419-2024, 2024. a, b, c, d, e
Boorsma, K., Greco, L., and Bedon, G.: Rotor wake engineering models for aeroelastic applications, Journal of Physics: Conference Series, 1037, 062 013, https://doi.org/10.1088/1742-6596/1037/6/062013, 2018. a
Bottasso, C., Campagnolo, F., Croce, A., and Tibaldi, C.: Optimization-based study of bend–twist coupled rotor blades for passive and integrated passive/active load alleviation, Wind Energy, 16, 1149–1166, https://doi.org/10.1002/we.1543, 2013. a
Bottasso, C. L., Chang, C.-S., Croce, A., Leonello, D., and Riviello, L.: Adaptive planning and tracking of trajectories for the simulation of maneuvers with multibody models, Computer Methods in Applied Mechanics and Engineering, 195, 7052–7072, https://doi.org/10.1016/j.cma.2005.03.011, 2006. a, b
Branlard, E., Jonkman, J., Brown, C., and Zhang, J.: A digital twin solution for floating offshore wind turbines validated using a full-scale prototype, Wind Energy Science, 9, 1–24, https://doi.org/10.5194/wes-9-1-2024, 2024a. a, b, c, d
Branlard, E., Jonkman, J., Lee, B., Jonkman, B., Singh, M., Mayda, E., and Dixon, K.: Improvements to the Blade Element Momentum Formulation of OpenFAST for Skewed Inflows, Journal of Physics: Conference Series, 2767, 022 003, https://doi.org/10.1088/1742-6596/2767/2/022003, 2024b. a, b
Branlard, E. S. P.: Flexible multibody dynamics using joint coordinates and the Rayleigh-Ritz approximation: The general framework behind and beyond Flex, Wind Energy, 22, 877–893, https://doi.org/10.1002/we.2327, 2019. a, b
Burden, F. and Winkler, D.: Bayesian Regularization of Neural Networks, Humana Press, Totowa, NJ, 23–42, ISBN 978-1-60327-101-1, https://doi.org/10.1007/978-1-60327-101-1_3, 2009. a
Chen, J., Pan, J., Li, Z., Zi, Y., and Chen, X.: Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals, Renewable Energy, 89, 80–92, https://doi.org/10.1016/j.renene.2015.12.010, 2016. a
Dimitrov, N., Kelly, M. C., Vignaroli, A., and Berg, J.: From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases, Wind Energy Science, 3, 767–790, https://doi.org/10.5194/wes-3-767-2018, 2018. a, b
Drécourt, J.-P., Madsen, H., and Rosbjerg, D.: Bias aware Kalman filters: Comparison and improvements, Advances in Water Resources, 29, 707–718, https://doi.org/10.1016/j.advwatres.2005.07.006, 2006. a, b
Evans, M., Han, T., and Shuchun, Z.: Development and validation of real time load estimator on Goldwind 6 MW wind turbine, Journal of Physics: Conference Series, 1037, 032021, https://doi.org/10.1088/1742-6596/1037/3/032021, 2018. a
Grewal, M. S. and Andrews, A. P.: Kalman Filtering: Theory and Practice Using Matlab, John Wiley & Sons, Inc., https://doi.org/10.1002/9781118984987, 2014. a, b
Guilloré, A., Campagnolo, F., and Bottasso, C. L.: A control-oriented load surrogate model based on sector-averaged inflow quantities: capturing damage for unwaked, waked, wake-steering and curtailed wind turbines, Journal of Physics: Conference Series, 2767, 032019, https://doi.org/10.1088/1742-6596/2767/3/032019, 2024. a
Hoghooghi, H.: Load Alleviation on Multi-Megawatt Wind Turbines Based on Measured Dynamic Response of Wind Turbine, Doctoral thesis, ETH Zurich, Zurich, https://doi.org/10.3929/ethz-b-000492802, 2021. a
Hoghooghi, H.: A wind turbine digital shadow for complex inflow conditions (Version V1), Zenodo [data set], https://doi.org/10.5281/zenodo.11519470, 2024. a
Hoghooghi, H., Chokani, N., and Abhari, R.: Effectiveness of individual pitch control on a 5MW downwind turbine, Renewable Energy, 139, 435–446, https://doi.org/10.1016/j.renene.2019.02.088, 2019a. a
Hoghooghi, H., Chokani, N., and Abhari, R. S.: Enhanced Yaw Stability of Downwind Turbines, Journal of Physics: Conference Series, 1356, 012020, https://doi.org/10.1088/1742-6596/1356/1/012020, 2019b. a
Hoghooghi, H., Chokani, N., and Abhari, R.: Optical measurements of multi-megawatt wind turbine dynamic response, Journal of Wind Engineering and Industrial Aerodynamics, 206, 104214, https://doi.org/10.1016/j.jweia.2020.104214, 2020a. a
Hoghooghi, H., Chokani, N., and Abhari, R. S.: Individual Blade Pitch Control for Extended Fatigue Lifetime of Multi-Megawatt Wind Turbines, Journal of Physics: Conference Series, 1618, 022 008, https://doi.org/10.1088/1742-6596/1618/2/022008, 2020b. a
Hoghooghi, H., Chokani, N., and Abhari, R. S.: A novel optimised nacelle to alleviate wind turbine unsteady loads, Journal of Wind Engineering and Industrial Aerodynamics, 219, 104817, https://doi.org/10.1016/j.jweia.2021.104817, 2021. a
Hoghooghi, H., Bertelè, M., Anand, A., and Bottasso, C. L.: A wind turbine digital shadow with tower and blade degrees of freedom – Preliminary results and comparison with a simple tower fore-aft model, Journal of Physics: Conference Series, 2767, 032026, https://doi.org/10.1088/1742-6596/2767/3/032026, 2024. a, b, c
IEC: IEC 61400-1 Wind turbines: Design requirements, International Electrotechnical Commision, Ed. 4.0, ISBN 9782832279724, 2019. 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, Mechanical Systems and Signal Processing, 68–69, 84–104, https://doi.org/10.1016/j.ymssp.2015.07.016, 2016. a
Jacquelin, E., Bennani, A., and Hamelin, P.: Force reconstruction: analysis and regularization of a deconvolution problem, Journal of Sound and Vibration, 265, 81–107, https://doi.org/10.1016/S0022-460X(02)01441-4, 2003. a
Jonkman, J. M., Wright, A. D., Hayman, G. J., and Robertson, A. N.: Full-System Linearization for Floating Offshore Wind Turbines in OpenFAST, ASME 2018 1st International Offshore Wind Technical Conference, V001T01A028, https://doi.org/10.1115/IOWTC2018-1025, 2018. a
Kim, K.-H., Bertelè, M., and Bottasso, C. L.: Wind inflow observation from load harmonics via neural networks: A simulation and field study, Renewable Energy, 204, 300–312, https://doi.org/10.1016/j.renene.2022.12.051, 2023. a, b, c, d
Liu, J., Wang, X., Xie, F., Wu, S., and Li, D.: Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network, Engineering Applications of Artificial Intelligence, 121, 106000, https://doi.org/10.1016/j.engappai.2023.106000, 2023. a, b
Loew, S. and Bottasso, C. L.: Lidar-assisted model predictive control of wind turbine fatigue via online rainflow counting considering stress history, Wind Energ. Sci., 7, 1605–1625, https://doi.org/10.5194/wes-7-1605-2022, 2022. a
Mendez Reyes, H., Kanev, S., Doekemeijer, B., and van Wingerden, J.-W.: Validation of a lookup-table approach to modeling turbine fatigue loads in wind farms under active wake control, Wind Energ. Sci., 4, 549–561, https://doi.org/10.5194/wes-4-549-2019, 2019. a
Natarajan, A.: Damage equivalent load synthesis and stochastic extrapolation for fatigue life validation, Wind Energ. Sci., 7, 1171–1181, https://doi.org/10.5194/wes-7-1171-2022, 2022. a
Nocedal, J. and Wright, S. J.: Numerical Optimization, Springer, New York, NY, 2nd edn., ISBN 978-0387303031, 2006. a
Noppe, N., Iliopoulos, A., Weijtjens, W., and Devriendt, C.: Full load estimation of an offshore wind turbine based on SCADA and accelerometer data, Journal of Physics: Conference Series, 753, 072025, https://doi.org/10.1088/1742-6596/753/7/072025, 2016. a
Olatunji, O. O., Adedeji, P. A., Madushele, N., and Jen, T.-C.: Overview of Digital Twin Technology in Wind Turbine Fault Diagnosis and Condition Monitoring, in: 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT), 201–207, https://doi.org/10.1109/ICMIMT52186.2021.9476186, 2021. a
Pacheco, J., Pimenta, F., Guimarães, S., Castro, G., Álvaro Cunha, Matos, J. C., and Magalhães, F.: Experimental evaluation of fatigue in wind turbine blades with wake effects, Engineering Structures, 300, 117140, https://doi.org/10.1016/j.engstruct.2023.117140, 2024. a
Schreiber, J., Bottasso, C. L., and Bertelè, M.: Field testing of a local wind inflow estimator and wake detector, Wind Energ. Sci., 5, 867–884, https://doi.org/10.5194/wes-5-867-2020, 2020a. a
Schreiber, J., Bottasso, C. L., and Bertelè, M.: Field testing of a local wind inflow estimator and wake detector, Wind Energ. Sci., 5, 867–884, https://doi.org/10.5194/wes-5-867-2020, 2020b. a
Schröder, L., Dimitrov, N. K., Verelst, D. R., and Sørensen, J. A.: Wind turbine site-specific load estimation using artificial neural networks calibrated by means of high-fidelity load simulations, Journal of Physics: Conference Series, 1037, 062027, https://doi.org/10.1088/1742-6596/1037/6/062027, 2018. a
Sepasgozar, S. M. E.: Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment, Buildings, 11, https://doi.org/10.3390/buildings11040151, 2021. a
Song, Z., Hackl, C. M., Anand, A., Thommessen, A., Petzschmann, J., Kamel, O., Braunbehrens, R., Kaifel, A., Roos, C., and Hauptmann, S.: Digital Twins for the Future Power System: An Overview and a Future Perspective, Sustainability, 15, 5259, https://doi.org/10.3390/su15065259, 2023. a
Surucu, O., Gadsden, S. A., and Yawney, J.: Condition Monitoring using Machine Learning: A Review of Theory, Applications, and Recent Advances, Expert Systems with Applications, 221, 119738, https://doi.org/10.1016/j.eswa.2023.119738, 2023. a, b
Vettori, S., Di Lorenzo, E., Peeters, B., and Chatzi, E.: A virtual sensing approach to operational modal analysis for wind turbine blades, in: Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021, edited by Mao, Z., 1st edn., River Publishers, New York, 187203, ISBN 978-87-438-0381-2, https://doi.org/10.1007/978-3-030-77348-9, 2022. a
Wan, E. and Van Der Merwe, R.: The unscented Kalman filter for nonlinear estimation, in: Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (ASSPCC), IEEE, 153158, https://doi.org/10.1109/ASSPCC.2000.882463, 2000. a
Wu, P., Liu, Y., Ferrari, R. M., and van Wingerden, J.-W.: Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis, IET Renewable Power Generation, 15, 4006–4018, https://doi.org/10.1049/rpg2.12319, 2021. a
Ziegler, L., Smolka, U., Cosack, N., and Muskulus, M.: Brief communication: Structural monitoring for lifetime extension of offshore wind monopiles: can strain measurements at one level tell us everything?, Wind Energ. Sci., 2, 469–476, https://doi.org/10.5194/wes-2-469-2017, 2017. a
Short summary
We formulate and demonstrate a new digital shadow (i.e., a virtual copy) for wind turbines. The digital shadow is designed in order to be capable of mirroring the response of the machine even in complex inflow conditions. Results from field measurements illustrate the ability of the shadow to estimate loads with good accuracy, even with minimal tuning.
We formulate and demonstrate a new digital shadow (i.e., a virtual copy) for wind turbines. The...
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