Articles | Volume 9, issue 6
https://doi.org/10.5194/wes-9-1419-2024
© Author(s) 2024. 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-9-1419-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jun 2024
Research article |
| 26 Jun 2024
| 26 Jun 2024
The rotor as a sensor – observing shear and veer from the operational data of a large wind turbine
Marta Bertelè
Wind Energy Institute, Technische Universität München, Boltzmannstr. 15, 85748 Garching bei München, Germany
Siemens Gamesa Renewable Energy, Beim Strohhause 17–31, 20097 Hamburg, Germany
Paul J. Meyer
Fraunhofer Institute for Wind Energy Systems (IWES), Am Seedeich 45, 27572 Bremerhaven, Germany
Carlo R. Sucameli
Wind Energy Institute, Technische Universität München, Boltzmannstr. 15, 85748 Garching bei München, Germany
Johannes Fricke
Fraunhofer Institute for Wind Energy Systems (IWES), Am Seedeich 45, 27572 Bremerhaven, Germany
Anna Wegner
Fraunhofer Institute for Wind Energy Systems (IWES), Am Seedeich 45, 27572 Bremerhaven, Germany
Julia Gottschall
Fraunhofer Institute for Wind Energy Systems (IWES), Am Seedeich 45, 27572 Bremerhaven, Germany
Wind Energy Institute, Technische Universität München, Boltzmannstr. 15, 85748 Garching bei München, Germany
Related authors
No articles found.
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.
Andebo Waza, Konrad Kandler, Franz Kanngießer, Julia Gottschall, Arne Leuzinger, Simon Speith, Natalia Sudarchikova, Sebastian Wahl, Wirginia Hepert, and Stephanie Fiedler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-733, https://doi.org/10.5194/essd-2026-733, 2026
Preprint under review for ESSD
Short summary
Short summary
Originating from North Africa, mineral dust, which is one of the most abundant aerosol types in the Earth’s atmosphere, is frequently transported across the Atlantic Ocean. To observe these dust transport different instruments, which provide information on both near-surface dust and lofted dust plumes, were deployed during two research cruises between January and March 2025 in the Atlantic Ocean. The presented data allows us to address existing knowledge gaps in trans-Atlantic dust transport.
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.
Lennart Vogt, Julia Gottschall, and Jasna Bogunović Jakobsen
Wind Energ. Sci., 11, 3587–3614, https://doi.org/10.5194/wes-11-3587-2026, https://doi.org/10.5194/wes-11-3587-2026, 2026
Short summary
Short summary
A combined dataset of sonic and cup anemometer measurements, together with synchronized short-range lidar observations, is used to derive turbulence characteristics, including integral length scales, auto-spectra, and spatial coherence. The instruments exhibit strong agreement and consistent trends across atmospheric stability. Deviations at high frequencies are attributed to spatial averaging effects. Empirical models fitted to the data closely reproduce the spectral estimates.
Jakob Mann, Ansh Patel, Mikael Sjöholm, Gunhild Rolighed Thorsen, Elliot Irving Simon, Lin-Ya Hung, and Julia Gottschall
Wind Energ. Sci., 11, 3153–3170, https://doi.org/10.5194/wes-11-3153-2026, https://doi.org/10.5194/wes-11-3153-2026, 2026
Short summary
Short summary
Turbulence over the ocean at heights relevant to modern offshore wind turbines, i.e., up to 300 m or more, is not well studied. It is important to know the properties of this turbulence because it is responsible for most of the dynamic loads on these structures, and consequently it is relevant to how the latter should be designed. This data description paper explains the efforts made to provide measurements of this offshore turbulence using five Doppler lidars.
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.
Astrid Lampert, Beatriz Cañadillas, Thomas Rausch, Lea Schmitt, Bughsin' Djath, Johannes Schulz-Stellenfleth, Andreas Platis, Kjell zum Berge, Ines Schäfer, Jens Bange, Lukas Gruchot, Thomas Neumann, Martin Dörenkämper, Bernhard Stoevesandt, Julia Gottschall, Lukas Vollmer, Stefan Emeis, Mares Barekzai, Simon Siedersleben, Martin Kühn, Gerald Steinfeld, Detlev Heinemann, Joachim Peinke, Hendrik Heißelmann, Jörge Schneemann, Gabriele Centurelli, Philipp Waldmann, and Konrad Bärfuss
Wind Energ. Sci., 11, 3057–3075, https://doi.org/10.5194/wes-11-3057-2026, https://doi.org/10.5194/wes-11-3057-2026, 2026
Short summary
Short summary
Two major aircraft measurement campaigns above the North Sea provide insights into modifications of the wind field and sea surface induced by wind farms. The aircraft performed transects at hub height upstream and downstream of wind farm clusters and identified different effects, e.g. how long it takes for the wind speed to recover after the wind farm, how changes across the coastline interact with wind energy, and if wind farms are well represented in numerical simulations.
Arianna Marie Jordan, Lin-Ya Hung, Gerrit Wolken-Möhlmann, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-114, https://doi.org/10.5194/wes-2026-114, 2026
Preprint under review for WES
Short summary
Short summary
Wind energy needs accurate airflow tools around turbines. We tested dual-Doppler radar at a research wind farm in Germany alongside lidar (a point-based wind sensor). The method matched lidar well at most heights and mapped wind shadows behind turbines, including wake meandering. Data quality depended on rainfall, with rainy periods giving the best coverage and fog the worst. This information is useful for implementation of dual-Doppler radar as a wind measurement tool for the industry.
Farkhondeh Rouholahnejad, Tanguy Lunel, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-99, https://doi.org/10.5194/wes-2026-99, 2026
Preprint under review for WES
Short summary
Short summary
Offshore wind farms rely on accurate measurements of wind and turbulence. Using three scanning lidars and an offshore meteorological mast, we investigated whether dual-Doppler lidar systems must measure exactly the same air volume to provide reliable turbulence estimates. We show that moderate beam separations can be tolerated under homogeneous offshore conditions and that the resulting differences provide useful information about spatial variability in the wind field.
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.
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.
Matthew Young, Matthew Docherty, Lin-Ya Hung, Andrew Oldroyd, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-94, https://doi.org/10.5194/wes-2026-94, 2026
Revised manuscript under review for WES
Short summary
Short summary
This study compares different ways of checking and correcting the pointing accuracy of scanning wind measurement systems used for offshore wind energy projects. Accurate positioning is essential for reliable wind measurements and reducing project risk. The results show that drone-based testing provides the most accurate results, while sea-surface methods offer a practical alternative when other reference points are unavailable.
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.
Filippo Campagnolo, Doruk Aktan, Davide Bortolin, Simone Tamaro, Franz V. Mühle, and Carlo L. Bottasso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-86, https://doi.org/10.5194/wes-2026-86, 2026
Revised manuscript accepted for WES
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.
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.
Hugo Rubio, Ville Vakkari, Martin Kühn, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-50, https://doi.org/10.5194/wes-2026-50, 2026
Preprint withdrawn
Short summary
Short summary
Offshore wind farms need accurate wind data, but collecting measurements at sea is often difficult and expensive. In this study, we investigate how wind measurements from a lidar mounted on a moving ship can improve the accuracy of commonly used reanalysis datasets. Using a statistical correction method, we are able to reduce errors in wind speed estimates. This efficient approach can make offshore wind planning and resource assessment more reliable in areas where measurements are limited.
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.
Stefan Ivanell, Bjarke T. Olsen, Antoine Mathieu, Cristina Mulet-Benzo, Abdul Haseeb Syed, Warit Chanprasert, Mikael Sjöholm, Jakob Mann, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-286, https://doi.org/10.5194/wes-2025-286, 2026
Preprint under review for WES
Short summary
Short summary
Modern GW-scale offshore wind farms face challenges from atmospheric dynamics. This study examines how boundary layer height (BLH) and large-scale turbulence affect efficiency and loads. Using WRF simulations, lidar data, and CFD modeling for a 100-turbine, 15 MW wind farm at three representative sites, we show that low BLH reduces performance. Turbulence-induced low-frequency fluctuations increase fatigue loads, underscoring the need to include BLH and turbulence in design models.
Warren Watson, Gerrit Wolken-Möhlmann, and Julia Gottschall
Wind Energ. Sci., 10, 2791–2820, https://doi.org/10.5194/wes-10-2791-2025, https://doi.org/10.5194/wes-10-2791-2025, 2025
Short summary
Short summary
In this study, we compare turbulence intensity measurements from two buoy-mounted wind lidars with data from a fixed lidar and a meteorological mast. Turbulence intensity is essential for understanding wind conditions but is often overestimated by floating systems due to wave motion. We applied a physics-based compensation to reduce these effects. Our findings show that motion compensation significantly improves accuracy, making floating lidar systems suitable for offshore wind site assessments.
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.
Cristina Mulet-Benzo, Andrew Black, and Julia Gottschall
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-155, https://doi.org/10.5194/wes-2025-155, 2025
Revised manuscript not accepted
Short summary
Short summary
Wind turbine wake modeling requires accurate representation of atmospheric conditions, especially the atmospheric boundary layer height (ABLH). Scanning lidars provide a viable solution for ABLH determination. This study assesses ABLH data from coastal and offshore scanning lidars, comparing it to ABLH derived from models typically used for wake modelling. The results demonstrate a general overestimation of the models compared to the lidar, and a bias sensitivity to the diurnal cycle.
Hugo Rubio, Daniel Hatfield, Charlotte Bay Hasager, Martin Kühn, and Julia Gottschall
Atmos. Meas. Tech., 18, 4949–4968, https://doi.org/10.5194/amt-18-4949-2025, https://doi.org/10.5194/amt-18-4949-2025, 2025
Short summary
Short summary
Due to the scarcity of offshore in situ observations, alternative data sources are essential for a reliable understanding of offshore winds. Therefore, this study delves into the world of satellite remote sensing (ASCAT) and numerical models (ERA5), exploring their capabilities and limitations in characterising offshore winds. This investigation evaluates these two datasets against measurements from a floating ship-based lidar, collected during a novel measurement campaign in the Baltic Sea.
Farkhondeh (Hanie) Rouholahnejad and Julia Gottschall
Wind Energ. Sci., 10, 143–159, https://doi.org/10.5194/wes-10-143-2025, https://doi.org/10.5194/wes-10-143-2025, 2025
Short summary
Short summary
In wind energy, precise wind speed prediction at hub height is vital. Our study in the Dutch North Sea reveals that the on-site-trained random forest model outperforms the global reanalysis data, ERA5, in accuracy and precision. Trained within a 200 km range, the model effectively extends the wind speed vertically but experiences bias. It also outperforms ERA5 corrected with measurements in capturing wind speed variations and fine wind patterns, highlighting its potential for site assessment.
Martin Georg Jonietz Alvarez, Warren Watson, and Julia Gottschall
Wind Energ. Sci., 9, 2217–2233, https://doi.org/10.5194/wes-9-2217-2024, https://doi.org/10.5194/wes-9-2217-2024, 2024
Short summary
Short summary
Offshore wind measurements are often affected by gaps. We investigated how these gaps affect wind resource assessments and whether filling them reduces their effect. We find that the effect of gaps on the estimated long-term wind resource is lower than expected and that data gap filling does not significantly change the outcome. These results indicate a need to reduce current wind data availability requirements for offshore measurement campaigns.
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.
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.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
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.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
Short summary
Short summary
A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
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.
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
Short summary
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.
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
Bertelè, M., Bottasso, C. L., and Schreiber, J.: Wind inflow observation from load harmonics: initial steps towards a field validation, Wind Energ. Sci., 6, 759–775, https://doi.org/10.5194/wes-6-759-2021, 2021. a, b
Bertelè, M., Meyer, P. J., Sucameli, C., Fricke, J., Wegner, A., Gottschall, J., and Bottasso, C. L.: Figures: The rotor as a sensor – Observing shear and veer from the operational data of a large wind turbine, Zenodo [data set], https://doi.org/10.5281/zenodo.8335021, 2023. a
Bishop, C. M.: Pattern recognition and machine learning, Springer, New York, ISBN 978-0-387-31073-2, 2006. a
Bottasso, C. L. and Riboldi, C. E. D.: Estimation of wind misalignment and vertical shear from blade loads, Renew. Energ., 62, 293–302, https://doi.org/10.1016/j.renene.2013.07.021, 2014. a
Bottasso, C. L., Cacciola, S., and Schreiber, J.: Local wind speed estimation, with application to wake impingement detection, Renew. Energ., 116, 155–168, https://doi.org/10.1016/j.renene.2017.09.044, 2018. a
Bromm, M., Vollmer, L., and Kühn, M.: Numerical investigation of wind turbine wake development in directionally sheared inflow, Wind Energy, 20, 381–395, https://doi.org/10.1002/we.2010, 2017. a
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
Clifton, A., Clive, P., Gottschall, J., Schlipf, D., Simley, E., Simmons, L., Stein, D., Trabucchi, D., Vasiljevic, N., and Würth, I.: IEA Wind Task 32 Wind Lidars – Identifying and Mitigating Barriers to the Adoption of Wind Lidars, Remote Sensing, 10, 406, https://doi.org/10.3390/rs10030406, 2018. a
Coleman, R. P. and Feingold, A. M.: Theory of self-excited mechanical oscillations of helicopter rotors with hinged blades, Technical Report, https://ntrs.nasa.gov/citations/19930092339 (last access: 21 June 2024), 1958. a
Eggleston, D. M. and Stoddard, F.: Wind turbine engineering design, Van Nostrand Reinhold, New York, https://www.osti.gov/biblio/5719832 (last access: 21 June 2024), 1987. a
Fleming, P. A., Gebraad, P. M., Lee, S., van Wingerden, J.-W., Johnson, K., Churchfield, M., Michalakes, J., Spalart, P., and Moriarty, P.: Evaluating techniques for redirecting turbine wakes using SOWFA, Renew. Energ., 70, 211–218, https://doi.org/10.1016/j.renene.2014.02.015, 2014. a
Fricke, J., Wiens, M., Requate, N., and Leimeister, M.: Python Framework for Wind Turbines Enabling Test Automation of MoWiT, Modelica Conferences, 181, 403–409, https://doi.org/10.3384/ecp21181403, 2021. a
Gebraad, P. M. O., Teeuwisse, F. W., van Wingerden, J. W., Fleming, P. A., Ruben, S. D., Marden, J. R., and Pao, L. Y.: Wind plant power optimization through yaw control using a parametric model for wake effects – a CFD simulation study, Wind Energy, 19, 95–114, https://doi.org/10.1002/we.1822, 2016. a
Giyanani, A., Sjöholm, M., Rolighed Thorsen, G., Schuhmacher, J., and Gottschall, J.: Wind speed reconstruction from three synchronized short-range WindScanner lidars in a large wind turbine inflow field campaign and the associated uncertainties, J. Phys. Conf. Ser., 2265, 022032, https://doi.org/10.1088/1742-6596/2265/2/022032, 2022. a
Gottschall, J., Courtney, M. S., Wagner, R., Jørgensen, H. E., and Antoniou, I.: Lidar profilers in the context of wind energy-a verification procedure for traceable measurements, Wind Energy, 15, 147–159, https://doi.org/10.1002/we.518, 2012. a
Huhn, M. L. and Gómez-Mejía, A. F.: Aeroelastic model validation with 8 MW field measurements: Influence of constrained turbulence with focus on power performance, J. Phys. Conf. Ser., 2265, 032058, https://doi.org/10.1088/1742-6596/2265/3/032058, 2022. a
Hung, L.-Y., Santos, P., and Gottschall, J.: A comprehensive procedure to process scanning lidar data for engineering wake model validation, J. Phys. Conf. Ser., 2265, 022091, https://doi.org/10.1088/1742-6596/2265/2/022091, 2022. a
Matlab: Deep Learning Toolbox, https://www.mathworks.com/help/deeplearning/ (last access: 20 July 2023), 2023. a
Meyer, P. J. and Gottschall, J.: Evaluation of the “fan scan” based on three combined nacelle lidars for advanced wind field characterisation, J. Phys. Conf. Ser., 2265, 022107, https://doi.org/10.1088/1742-6596/2265/2/022107, 2022. a, b, c
Meyers, J., Bottasso, C., Dykes, K., Fleming, P., Gebraad, P., Giebel, G., Göçmen, T., and van Wingerden, J.-W.: Wind farm flow control: prospects and challenges, Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, 2022. a, b
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, 2020. a, b
Soltani, M. N., Knudsen, T., Svenstrup, M., Wisniewski, R., Brath, P., Ortega, R., and Johnson, K.: Estimation of Rotor Effective Wind Speed: A Comparison, IEEE T. Control Syst. T., 21, 1155–1167, https://doi.org/10.1109/TCST.2013.2260751, 2013. a, b
Vollmer, L., Steinfeld, G., Heinemann, D., and Kühn, M.: Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study, Wind Energ. Sci., 1, 129–141, https://doi.org/10.5194/wes-1-129-2016, 2016. a, b
Wegner, A., Huhn, M. L., Mechler, S., and Thomas, P.: Identification of torsional frequencies of a large rotor blade based on measurement and simulation data, J. Phys. Conf. Ser., 2265, 032021, https://doi.org/10.1088/1742-6596/2265/3/032021, 2022. a, b
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.
A neural observer is used to estimate shear and veer from the operational data of a large wind...
Altmetrics
Final-revised paper
Preprint