Articles | Volume 7, issue 4
https://doi.org/10.5194/wes-7-1605-2022
© 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-7-1605-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Aug 2022
Research article |
| 03 Aug 2022
| 03 Aug 2022
Lidar-assisted model predictive control of wind turbine fatigue via online rainflow counting considering stress history
Stefan Loew
Wind Energy Institute, Technical University of Munich, 85748
Garching b. München, Germany
Wind Energy Institute, Technical University of Munich, 85748
Garching b. München, Germany
Related authors
No articles found.
Iasonas Tsaklis, Vasilis Pettas, Adrien Guilloré, Nicolau Conti Gost, David Astrain-Juangarcia, Abhinav Anand, Andreas Vad, Anik H. Shah, Ion Lizarraga-Saenz, Maria Aparicio-Sanchez, Irene Eguinoa, Carlo L. Bottasso, Nikolay Dimitrov, and Tuhfe Göçmen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-158, https://doi.org/10.5194/wes-2026-158, 2026
Preprint under review for WES
Short summary
Short summary
This study provides an openly accessible dataset of wind turbine simulations and a reproducible framework for developing and comparing machine-learning models that predict turbine operation and structural loads. By covering diverse operating conditions, these resources help researchers understand turbine behaviour, test new prediction methods, and support future wind energy research. The work promotes transparent, consistent, and accessible model development.
Filippo Campagnolo, Hamit Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso
Wind Energ. Sci., 11, 4005–4040, https://doi.org/10.5194/wes-11-4005-2026, https://doi.org/10.5194/wes-11-4005-2026, 2026
Short summary
Short summary
This paper presents an open-access wind-tunnel dataset of actuated, scaled wind turbines with time-resolved measurements of loads, actuator commands, inflow conditions, and wakes. It covers multiple wake-control strategies (yaw steering, derating, Helix, dynamic yaw, Pulse, pitch control). Combined with numerical models, the dataset enables benchmarking, controller analysis, and validation of aeroelastic and wake models for wind farm control research.
Adrien Guilloré, Andreas Vad, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-191, https://doi.org/10.5194/wes-2026-191, 2026
Preprint under review for WES
Short summary
Short summary
Accurate wind turbine load prediction is essential for wind farm design, optimization, and control. This study addresses both the design and implementation of location-agnostic, control-oriented load surrogate models. A key finding is that a finite level of physical complexity is sufficient for a general model to achieve converged performance. The results provide practical guidelines for reliable, computationally efficient fatigue load prediction across different implementation environments.
Abhinav Anand, Robert Braunbehrens, Adrien Guilloré, and Carlo L. Bottasso
Wind Energ. Sci., 11, 3949–3981, https://doi.org/10.5194/wes-11-3949-2026, https://doi.org/10.5194/wes-11-3949-2026, 2026
Short summary
Short summary
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.
Matteo Baricchio, Samuel Kainz, Jan-Willem van Wingerden, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-178, https://doi.org/10.5194/wes-2026-178, 2026
Preprint under review for WES
Short summary
Short summary
Layout and control of wind farms are traditionally optimized sequentially for economic objectives, often overlooking environmental impacts. This study investigates the effect of introducing a carbon-related objective into layout, control, and layout-control co-design optimization. Results for a large-scale offshore wind farm show that using an environmental objective can achieve economic performance comparable to that of conventional economic optimization, while the resulting layouts may differ.
Hadi Hoghooghi and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-156, https://doi.org/10.5194/wes-2026-156, 2026
Preprint under review for WES
Short summary
Short summary
Wind turbines operate under changing weather conditions, making it difficult to accurately estimate structural stress. This study improves a digital model by combining engineering knowledge with learning from real turbine measurements. The new approach significantly reduced prediction errors and remained reliable across different conditions. More accurate load estimates can support better turbine monitoring and maintenance planning.
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 García-Santiago, Jonas Schulte, Knut S. Seim, M. Paul van der Laan, Kisorthman Vimalakanthan, and Adam Wise
Wind Energ. Sci., 11, 3107–3135, https://doi.org/10.5194/wes-11-3107-2026, https://doi.org/10.5194/wes-11-3107-2026, 2026
Short summary
Short summary
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.
Hadi Hoghooghi and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-102, https://doi.org/10.5194/wes-2026-102, 2026
Manuscript not accepted for further review
Short summary
Short summary
Reliable wind turbine fatigue load estimates are key to assessing structural integrity and service life. This study integrates a simplified simulation model with machine learning to enhance predictions while protecting industrial design confidentiality. It achieves DEL errors under 5 % across varied conditions and performs well on simulated and field data, supporting improved monitoring, maintenance, and lifetime assessment.
Carlo Rosario Sucameli, Marta Bertelè, Robert Braunbehrens, Filippo Campagnolo, Simone Tamaro, Paul Hulsman, and Carlo Luigi Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-93, https://doi.org/10.5194/wes-2026-93, 2026
Preprint under review for WES
Short summary
Short summary
Wind turbines extract energy by slowing the air, so downstream turbines receive less wind and produce less power, a phenomenon called wake. Wind is not uniform: it varies in both speed and direction with height, also affecting downstream turbines. Using a year of measurements and simulations, we found that both effects influence the power production of downstream turbines by pushing the air sideways. We built improved models to capture these effects, enabling better wind farm control strategies.
Abhinav Anand and Carlo L. Bottasso
Wind Energ. Sci., 11, 1989–2008, https://doi.org/10.5194/wes-11-1989-2026, https://doi.org/10.5194/wes-11-1989-2026, 2026
Short summary
Short summary
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.
Simone Tamaro, Davide Bortolin, Filippo Campagnolo, Franz V. Mühle, and Carlo L. Bottasso
Wind Energ. Sci., 11, 1607–1630, https://doi.org/10.5194/wes-11-1607-2026, https://doi.org/10.5194/wes-11-1607-2026, 2026
Short summary
Short summary
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.
Andre Thommessen, Abhinav Anand, Carlo L. Bottasso, and Christoph M. Hackl
Wind Energ. Sci., 11, 1399–1428, https://doi.org/10.5194/wes-11-1399-2026, https://doi.org/10.5194/wes-11-1399-2026, 2026
Short summary
Short summary
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.
Marinos Manolesos, Sandrine Aubrun, Christian Bak, Paolo Bettini, Filippo Campagnolo, Alessandro Croce, Arslan Salim Dar, Michael Hölling, Stefan Ivanell, Konstantinos Kellaris, Miguel Alfonso Mendez, Pierluigi Montinari, Franz Mühle, George Papadakis, Fernardo Porté-Agel, Daniele Ragni, Sebastiano Randino, Andrea Sciacchitano, Lorenzo Schena, Gerard Schepers, Antonio Segalini, Wei Yu, and Carlo Luigi Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-51, https://doi.org/10.5194/wes-2026-51, 2026
Manuscript not accepted for further review
Short summary
Short summary
As wind turbines grow to unprecedented sizes, ensuring their reliability requires advanced testing. We reviewed the current state of wind tunnel experiments, examining how scaled physical models are evaluated for wind flow, flexibility, and noise. We conclude that while laboratory testing remains essential, it is most powerful when integrated with computer simulations and real-world data. This combined approach will ultimately drive the design of more efficient and sustainable wind energy.
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
Revised manuscript under review for WES
Short summary
Short summary
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
Short summary
Short summary
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.
Hadi Hoghooghi and Carlo L. Bottasso
Wind Energ. Sci., 11, 373–393, https://doi.org/10.5194/wes-11-373-2026, https://doi.org/10.5194/wes-11-373-2026, 2026
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Cited articles
Abbas, N. J., Zalkind, D. S., Pao, L., and Wright, A.: A reference open-source controller for fixed and floating offshore wind turbines, Wind Energ. Sci., 7, 53–73, https://doi.org/10.5194/wes-7-53-2022, 2022. a
Anand, A.: Optimal Control of Battery Energy Storage System for Grid
Integration of Wind Turbines, Master's thesis, TU Munich, Munich, 2020. a
ASTM International: Standard practices for cycle counting in fatigue analysis
(ASTM 1049-85), https://doi.org/10.1520/E1049-85R17, 1985. a, b
Barradas-Berglind, J. d. J., Wisniewski, R., and Soltani, M.: Fatigue damage
estimation and data-based control for wind turbines, IET Control Theory &
Applications, 9, 1042–1050, https://doi.org/10.1049/iet-cta.2014.0730, 2015. a, b, c
Barradas-Berglind, J. J. and Wisniewski, R.: Representation of fatigue for wind
turbine control, Wind Energy, 19, 2189–2203, https://doi.org/10.1002/we.1975, 2016. a, b
Bottasso, C. and Croce, A.: Cascading Kalman observers of structural flexible
and wind states for wind turbine control: Scientific Report DIA-SR 09-02,
Dipartimento di Ingegneria Aerospaziale, Politecnico di Milano, https://www.researchgate.net/publication/228941698_Cascading_Kalman_observers_of_structural_flexible_and_wind_states_for_wind_turbine_control (last access: 10 October 2021), 2009. a
Bottasso, C. L., Pizzinelli, P., Riboldi, C., and Tasca, L.: LiDAR-enabled
model predictive control of wind turbines with real-time capabilities,
Renew. Energ., 71, 442–452, https://doi.org/10.1016/j.renene.2014.05.041, 2014. a, b
Canet, H., Löw, S., and Bottasso, C. L.: Lidar-assisted control in wind
turbine design: Where are the potential benefits?, J. Phys. Conf. Ser., 1618, 042020, https://doi.org/10.1088/1742-6596/1618/4/042020,
2020. a, b
Dickler, S., Wintermeyer-Kallen, T., Zierath, J., Bockhahn, R., Machost, D.,
Konrad, T., and Abel, D.: Full-scale field test of a model predictive control
system for a 3 MW wind turbine, Forsch. Ingenieurwes., 85, 313–323,
https://doi.org/10.1007/s10010-021-00467-w, 2021. a
Diehl, M. and Gros, S.: Numerical Optimal Control: Lecture manuscript,
University of Freiburg, Freiburg and Gothenburg,
https://www.syscop.de/files/2020ss/NOC/book-NOCSE.pdf (last access: 10 October 2021), 2020. a
Do, H. and Söffker, D.: Wind Turbine Lifetime Control Using Structural
Health Monitoring and Prognosis, IFAC PapersOnLine, 53, 12669–12674,
https://doi.org/10.1016/j.ifacol.2020.12.1847, 2020. a
Evans, M. A., Cannon, M., and Kouvaritakis, B.: Robust MPC Tower Damping for
Variable Speed Wind Turbines, IEEE T. Contr. Syst. T., 23, 290–296, https://doi.org/10.1109/TCST.2014.2310513, 2015. a, b, c
Gros, S.: An Economic NMPC Formulation for Wind Turbine Control, in: 52nd IEEE
Conference on Decision and Control, 1001–1006, IEEE, Firenze, Italy, 10–13 December 2013, https://doi.org/10.1109/CDC.2013.6760013, 2013. a, b
Gros, S. and Schild, A.: Real-time economic nonlinear model predictive control
for wind turbine control, Int. J. Control, 90, 2799–2812, 10–13 December 2013, https://doi.org/10.1080/00207179.2016.1266514, 2017. a, b, c
Gros, S., Vukov, M., and Diehl, M.: A real-time MHE and NMPC scheme for wind
turbine control, in: 2013 IEEE 52nd Annual Conference on Decision and Control
(CDC), 1007–1012, IEEE, Piscataway, NJ, USA, https://doi.org/10.1109/CDC.2013.6760014,
2013. a, b, c
Grüne, L. and Pannek, J.: Nonlinear Model Predictive Control: Theory and
Algorithms, Communications and Control Engineering, Springer International
Publishing, Cham, Switzerland, 2nd edn.,
https://doi.org/10.1007/978-3-319-46024-6, 2017. a
Haibach, E.: Betriebsfestigkeit: Verfahren und Daten zur Bauteilberechnung,
VDI-Buch, Springer, Berlin, 3 edn., https://doi.org/10.1007/3-540-29364-7, 2006. a
Hau, E.: Windkraftanlagen: Grundlagen. Technik. Einsatz. Wirtschaftlichkeit,
Springer Berlin Heidelberg, Berlin, Heidelberg, 6 edn.,
https://doi.org/10.1007/978-3-662-53154-9, 2017. a
Heinrich, C., Khalil, M., Martynov, K., and WEVER, U.: Online remaining
lifetime estimation for structures, Mech. Syst. Signal Pr.,
119, 312–327, https://doi.org/10.1016/j.ymssp.2018.09.028, 2019. a
Huang, R., Biegler, L. T., and Patwardhan, S. C.: Fast Offset-Free Nonlinear
Model Predictive Control Based on Moving Horizon Estimation, Ind. Eng. Chem. Res., 49, 7882–7890, https://doi.org/10.1021/ie901945y,
2010. a, b
Jassmann, U., Zierath, J., Dickler, S., and Abel, D.: Model Predictive Wind
Turbine Control for Load Alleviation and Power Leveling in Extreme Operation
conditions, in: 2016 IEEE Conference on Control Applications (CCA), IEEE, 19–22 September 2016, Buenos Aires, Argentina, https://doi.org/10.1109/CCA.2016.7587997, 2016. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW
Reference Wind Turbine for Offshore System Development: Technical Report
NREL/TP-500-38060, National Renewable Energy Laboratory,
https://doi.org/10.2172/947422, 2009. a, b, c, d
Köhler, M., Jenne, S., Pötter, K., and Zenner, H.: Zählverfahren
und Lastannahme in der Betriebsfestigkeit, Springer, Berlin, Heidelberg,
https://doi.org/10.1007/978-3-642-13164-6, 2012. a
Loew, S.: Matlab figure files for key result plots, https://doi.org/10.5281/zenodo.6600688, Zenodo [data set], 2022a. a
Loew, S.: MATLAB function and a test script for the PORFC parameter generation, https://doi.org/10.5281/zenodo.6600832, Zenodo [code], 2022b. a
Loew, S. and Obradovic, D.: Formulation of Fatigue Dynamics as Hybrid Dynamical
System for Model Predictive Control, IFAC PapersOnLine, 53, 6616–6623,
https://doi.org/10.1016/j.ifacol.2020.12.080, 2020. a
Loew, S., Obradovic, D., and Bottasso, C. L.: Direct Online Rainflow-counting
and Indirect Fatigue Penalization Methods for Model Predictive Control, in:
2019 18th European Control Conference (ECC), 3371–3376, IEEE,
25–28 June 2019, Naples, Italy, https://doi.org/10.23919/ECC.2019.8795911, 2019. a
Loew, S., Obradovic, D., Anand, A., and Szabo, A.: Stage Cost Formulations of
Online Rainflow-counting for Model Predictive Control of Fatigue, in: 2020
European Control Conference (ECC), 475–482, 12–15 May 2020, St. Petersburg, Russia, https://doi.org/10.23919/ECC51009.2020.9143939, 2020a. a, b
Loew, S., Obradovic, D., and Bottasso, C. L.: Model predictive control of wind
turbine fatigue via online rainflow-counting on stress history and
prediction, J. Phys. Conf. Ser., 1618, 22041,
https://doi.org/10.1088/1742-6596/1618/2/022041, 2020b. a, b, c, d
Löw, S. and Obradovic, D.: Real-time Implementation of Nonlinear Model
Predictive Control for Mechatronic Systems Using a Hybrid Model, atp magazin,
60, 46–53, https://doi.org/10.17560/atp.v60i09.2359, 2018. a
Manwell, J. F., McGowan, J. G., and Rogers, A. L.: Wind energy explained:
Theory, design and application, John Wiley & Sons, Chichester, England,
https://doi.org/10.1002/0470846127, 2002. a
Miner, M. A.: Culmulative Damage in Fatigue, J. Appl. Mech., 12,
A159–A164, https://doi.org/10.1115/1.4009458, 1945. a
Raach, S. and Schlipf, D.: Cross-tool realistic lidar simulations: Presentation
at the IEA Wind Task 32 workshop on Certification of Lidar-assisted control
applications,
https://www.ieawindtask32.org/ws08-internal-documents/ (last access: 10 October 2021), 2018. a
Ritter, B.: Nonlinear State Estimation and Noise Adaptive Kalman Filter Design
for Wind Turbines, Dissertation, Technische Universität Darmstadt,
Darmstadt, https://doi.org/10.25534/tuprints-00011785, 2020. a
Sanchez, H., Escobet, T., Puig, V., and Odgaard, P. F.: Health-aware Model
Predictive Control of Wind Turbines using Fatigue Prognosis,
IFAC PapersOnLine, 48, 1363–1368, https://doi.org/10.1016/j.ifacol.2015.09.715, 2015. a
Schlipf, D.: Lidar-assisted control concepts for wind turbines, Dissertation,
University of Stuttgart, Stuttgart, Germany, https://doi.org/10.18419/OPUS-8796,
2016. a, b, c
Schlipf, D. and Raach, S.: Turbulent Extreme Event Simulations for
Lidar-Assisted Wind Turbine Control, J. Phys. Conf. Ser.,
753, 052011, https://doi.org/10.1088/1742-6596/753/5/052011, 2016. a
Schlipf, D., Schlipf, D. J., and Kühn, M.: Nonlinear model predictive
control of wind turbines using LIDAR, Wind Energy, 16, 1107–1129,
https://doi.org/10.1002/we.1533, 2013. a, b, c
Shi, Y., Xu, B., Tan, Y., and Zhang, B.: A Convex Cycle-based Degradation Model
for Battery Energy Storage Planning and Operation, in: 2018 Annual American
Control Conference (ACC), 4590–4596, IEEE, 27–29 June 2018, Milwaukee, WI, USA, https://doi.org/10.23919/ACC.2018.8431814, 2018.
a
Simon, D.: Kalman filtering with state constraints: a survey of linear and
nonlinear algorithms, IET Control Theory A., 4, 1303–1318,
https://doi.org/10.1049/iet-cta.2009.0032, 2010. a
Sinner, M., Petrovic, V., Langidis, A., Neuhaus, L., Holling, M., Kuhn, M., and
Pao, L. Y.: Experimental Testing of a Preview-Enabled Model Predictive
Controller for Blade Pitch Control of Wind Turbines, IEEE T. Contr. Syst. T., 583–597, https://doi.org/10.1109/TCST.2021.3070342, 2021. a
Verschueren, R., Frison, G., Kouzoupis, D., Frey, J., van Duijkeren, N.,
Zanelli, A., Novoselnik, B., Albin, T., Quirynen, R., and Diehl, M.:
acados – a modular open-source framework for fast embedded optimal control,
Mathematical Programming Computation, 14, 147–183, https://doi.org/10.1007/s12532-021-00208-8, 2021. a
Short summary
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.
This publication presents methods to improve the awareness and control of material fatigue for...
Altmetrics
Final-revised paper
Preprint