Articles | Volume 9, issue 7
https://doi.org/10.5194/wes-9-1507-2024
© 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-9-1507-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jul 2024
Research article |
| 12 Jul 2024
| 12 Jul 2024
Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm
Diederik van Binsbergen
CORRESPONDING AUTHOR
Department of Mechanical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
Department of Marine Technology, NTNU, Jonsvannsveien 82, Trondheim, 7050, Norway
Pieter-Jan Daems
Department of Mechanical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
Timothy Verstraeten
Department of Mechanical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
Amir R. Nejad
Department of Marine Technology, NTNU, Jonsvannsveien 82, Trondheim, 7050, Norway
Jan Helsen
Department of Mechanical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-120, https://doi.org/10.5194/wes-2026-120, 2026
Preprint under review for WES
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A key challenge for offshore wind farms is to quantify wakes reliably from turbine measurements. This study develops a statistical approach that learns from measured turbine data how these wakes vary between wind farms and transfers that knowledge to new sites with quantified uncertainty. It also assesses how atmospheric conditions affect wake recovery. The results show that stable conditions lead to slower recovery and larger predicted losses, while unstable conditions support faster recovery.
Rebeca Marini, Konstantinos Vratsinis, Pieter-Jan Daems, Timothy Verstraeten, Etienne Cheynet, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-117, https://doi.org/10.5194/wes-2026-117, 2026
Preprint under review for WES
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We studied which environmental conditions affect the power produced by a single wind turbine without interference from nearby turbines. Using measured turbine and weather data together with machine learning, we found that the difference between air and sea temperature has a meaningful influence but is not included in commonly used power corrections. This could improve power predictions and wind resource assessments, and the method can be extended to larger groups of turbines.
Faras Jamil, Cedric Peeters, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-83, https://doi.org/10.5194/wes-2026-83, 2026
Preprint under review for WES
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Maintenance costs are a major challenge for the wind energy industry. This research introduces an automated artificial intelligence method to monitor drivetrain health. A model trained on healthy data analyses vibration patterns to identify deviations signalling potential faults. It is validated on offshore farms and has detected all 19 confirmed fault cases. It provides a single health indicator to help operators plan efficient maintenance strategies, reducing the Levelised Cost of Energy.
Vasudev Gupta and Amir R. Nejad
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-105, https://doi.org/10.5194/wes-2026-105, 2026
Preprint under review for WES
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This study improves how wind turbine drivetrains are designed by linking structural behavior with long-term critical fatigue effects. By optimizing the main bearing-shaft assembly, for minimum mass, it is shown that design choices strongly affect efficiency and durability. Results reveal that some configurations are limited by ultimate, while others are limited by fatigue, highlighting the need to consider both in novel designs to build lighter and more reliable wind turbines.
Kayacan Kestel, Xavier Chesterman, Donatella Zappalá, Simon Watson, Mingxin Li, Edward Hart, James Carroll, Yolanda Vidal, Amir R. Nejad, Shawn Sheng, Yi Guo, Matthias Stammler, Florian Wirsing, Ahmed Saleh, Nico Gregarek, Thao Baszenski, Thomas Decker, Martin Knops, Georg Jacobs, Benjamin Lehmann, Florian König, Ines Pereira, Pieter-Jan Daems, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 11, 2103–2155, https://doi.org/10.5194/wes-11-2103-2026, https://doi.org/10.5194/wes-11-2103-2026, 2026
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Felix C. Mehlan and Amir R. Nejad
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-90, https://doi.org/10.5194/wes-2026-90, 2026
Preprint under review for WES
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Konstantinos Vratsinis, Rebeca Marini, Pieter-Jan Daems, Lukas Pauscher, Jeroen van Beeck, and Jan Helsen
Wind Energ. Sci., 11, 1803–1820, https://doi.org/10.5194/wes-11-1803-2026, https://doi.org/10.5194/wes-11-1803-2026, 2026
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Matthias Stammler, Faras Jamil, Xinrun Liu, Jens Jo Matthys, Nikhil Sudhakaran, Cédric Peeters, Asger Bech Abrahamsen, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-81, https://doi.org/10.5194/wes-2026-81, 2026
Preprint under review for WES
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This study compares traditional and artificial intelligence supported methods for detecting damage in rotating machine parts. Bearings were tested under controlled conditions to see how well each method identified developing problems. The artificial intelligence supported method detected damage as early as, or earlier than, established techniques while needing far less expert attention. This supports automated monitoring of wind turbines, enabling earlier maintenance and lower operating costs.
Xavier Chesterman, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 11, 1163–1183, https://doi.org/10.5194/wes-11-1163-2026, https://doi.org/10.5194/wes-11-1163-2026, 2026
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This paper presents a sensor-error-robust methodology for failure prediction in wind turbines. Sensor malfunctions pose a significant challenge for data-driven prognostic approaches. The proposed method employs a masked autoencoder that enables selective deactivation of signals. Evaluation is done using data from several operational offshore wind farms. Results demonstrate that the model effectively mitigates the impact of sensor errors while maintaining high accuracy in failure prediction.
Ryota Wada, Amir R. Nejad, Kazuhiro Iijima, Junji Shimazaki, Mihaela Ibrion, Shinnosuke Wanaka, Hideo Nomura, Yoshitaka Mizushima, Takuya Nakashima, and Ken Takagi
Wind Energ. Sci., 11, 1013–1056, https://doi.org/10.5194/wes-11-1013-2026, https://doi.org/10.5194/wes-11-1013-2026, 2026
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Jakob Gebel, Pieter-Jan Daems, Diederik van Binsbergen, Veronica Liverud Krathe, Amir R. Nejad, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-13, https://doi.org/10.5194/wes-2026-13, 2026
Preprint under review for WES
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Ashkan Rezaei and Amir Rasekhi Nejad
Wind Energ. Sci., 10, 2947–2963, https://doi.org/10.5194/wes-10-2947-2025, https://doi.org/10.5194/wes-10-2947-2025, 2025
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We analyzed the reliability of wind turbine blade bearings under extreme turbulence wind conditions to improve their safety and performance. Using simulations, we studied how factors like turbulence and design variations affect failure probability. Our findings show that current standards may underestimate these risks and highlight the need for stricter controls on critical design parameters.
Ivo Vervlimmeren, Xavier Chesterman, Timothy Verstraeten, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 10, 2615–2637, https://doi.org/10.5194/wes-10-2615-2025, https://doi.org/10.5194/wes-10-2615-2025, 2025
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Yuksel R. Alkarem, Ian Ammerman, Kimberly Huguenard, Richard W. Kimball, Babak Hejrati, Amrit Verma, Amir R. Nejad, Reza Hashemi, and Stephan Grilli
Wind Energ. Sci., 10, 2475–2488, https://doi.org/10.5194/wes-10-2475-2025, https://doi.org/10.5194/wes-10-2475-2025, 2025
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Faras Jamil, Cédric Peeters, Timothy Verstraeten, and Jan Helsen
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A hybrid fault detection method is proposed, which combines physical domain knowledge with machine learning models to automatically detect mechanical faults in wind turbine drivetrain components. It offers detailed insights for experts while giving operators a high-level overview of the machine's health to assist in planning effective maintenance strategies. It was validated on multiple years of wind farm data and the potential faults were accurately predicted, which was confirmed by experts.
Simon Daenens, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 10, 1137–1152, https://doi.org/10.5194/wes-10-1137-2025, https://doi.org/10.5194/wes-10-1137-2025, 2025
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This study presents a novel model for predicting wind turbine power output at a high temporal resolution in wind farms using a hybrid graph neural network (GNN) and long short-term memory (LSTM) architecture. By modeling the wind farm as a graph, the model captures both spatial and temporal dynamics, outperforming traditional power curve methods. Integrated with a normal behavior model (NBM) framework, the model effectively identifies and analyzes power loss events.
Stijn Ally, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 10, 779–812, https://doi.org/10.5194/wes-10-779-2025, https://doi.org/10.5194/wes-10-779-2025, 2025
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Jakob Gebel, Ashkan Rezaei, Adithya Vemuri, Veronica Liverud Krathe, Pieter-Jan Daems, Jens Jo Matthys, Jonathan Sterckx, Konstantinos Vratsinis, Kayacan Kestel, Amir R. Nejad, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-173, https://doi.org/10.5194/wes-2024-173, 2025
Preprint under review for WES
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A simulation model of a deployed offshore wind turbine was developed using real-world measurement data. The method shows how to obtain, update and validate a simulation model and allows to improve the efficiency and longevity of offshore wind turbines and support operation and maintenance decisions. Simulations were conducted to analyze the effects of turbulence and wind patterns on turbine lifespan, providing insights to improve maintenance planning and reduce operational costs.
Rebeca Marini, Konstantinos Vratsinis, Kayacan Kestel, Jonathan Sterckx, Jens Matthys, Pieter-Jan Daems, Timothy Verstraeten, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-9, https://doi.org/10.5194/wes-2025-9, 2025
Revised manuscript not accepted
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This work evaluated the wind profile in a Belgian offshore zone. The estimated wind profile was made using measurements that allow for reconstruction at heights along the rotor area. The IEC standard defines these profiles as a 1/7th power law, which is proven not to occur 100 % of the time. It is also possible to infer that there will be differences when using different wind profiles for load assessment, as more realistic profiles can lead to a better assessment of the wind turbine's lifetime.
Felix C. Mehlan and Amir R. Nejad
Wind Energ. Sci., 10, 417–433, https://doi.org/10.5194/wes-10-417-2025, https://doi.org/10.5194/wes-10-417-2025, 2025
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A digital twin is a virtual representation that mirrors the wind turbine's real behavior through simulation models and sensor measurements and can assist in making key decisions such as planning the replacement of parts. These models and measurements are, of course, not perfect and only give an incomplete picture of the real behavior. This study investigates how large the uncertainty of such models and measurements is and to what extent it affects the decision-making process.
Ali Dibaj, Mostafa Valavi, and Amir R. Nejad
Wind Energ. Sci., 9, 2063–2086, https://doi.org/10.5194/wes-9-2063-2024, https://doi.org/10.5194/wes-9-2063-2024, 2024
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This study emphasizes the need for effective condition monitoring in permanent magnet offshore wind generators to tackle issues like demagnetization and eccentricity. Utilizing a machine learning model and high-resolution measurements, we explore methods of early fault detection. Our findings indicate that flux monitoring with affordable, easy-to-install stray flux sensors with frequency information offers a promising fault detection strategy for large megawatt-scale offshore wind generators.
Yuksel Rudy Alkarem, Kimberly Huguenard, Richard Kimball, Spencer Hallowell, Amrit Verma, Erin Bachynski-Polić, and Amir Nejad
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-67, https://doi.org/10.5194/wes-2024-67, 2024
Preprint withdrawn
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This research is a "less-is-more" demonstration of a novel concept that boost the efficiency of floating wind farms while maintaining fewer number of mooring line/anchors, reducing cost and the large footprint wind farms can have over the ocean bed and the water column. The novelty of this work lies in the passive wake steering method to enhance annual energy production and in integrating that with configurations that allow shared/multiline anchoring potential.
Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 8, 893–924, https://doi.org/10.5194/wes-8-893-2023, https://doi.org/10.5194/wes-8-893-2023, 2023
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This paper reviews and implements several techniques that can be used for condition monitoring and failure prediction for wind turbines using SCADA data. The focus lies on techniques that respond to requirements of the industry, e.g., robustness, transparency, computational efficiency, and maintainability. The end result of this research is a pipeline that can accurately detect three types of failures, i.e., generator bearing failures, generator fan failures, and generator stator failures.
Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022, https://doi.org/10.5194/wes-7-1869-2022, 2022
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The sensitivity of the WRF mesoscale modeling framework in accurately representing and predicting wind-farm-level environmental variables for three extreme weather events over the Belgian North Sea is investigated in this study. The overall results indicate highly sensitive simulation results to the type and combination of physics parameterizations and the type of the weather phenomena, with indications that scale-aware physics parameterizations better reproduce wind-related variables.
Amir R. Nejad, Jonathan Keller, Yi Guo, Shawn Sheng, Henk Polinder, Simon Watson, Jianning Dong, Zian Qin, Amir Ebrahimi, Ralf Schelenz, Francisco Gutiérrez Guzmán, Daniel Cornel, Reza Golafshan, Georg Jacobs, Bart Blockmans, Jelle Bosmans, Bert Pluymers, James Carroll, Sofia Koukoura, Edward Hart, Alasdair McDonald, Anand Natarajan, Jone Torsvik, Farid K. Moghadam, Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, https://doi.org/10.5194/wes-7-387-2022, 2022
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This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the energy conversion systems transferring the kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps.
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Short summary
Wind farm yield assessment often relies on analytical wake models. Calibrating these models can be challenging due to the stochastic nature of wind. We developed a calibration framework that performs a multi-phase optimization on the tuning parameters using time series SCADA data. This yields a parameter distribution that more accurately reflects reality than a single value. Results revealed notable variation in resultant parameter values, influenced by nearby wind farms and coastal effects.
Wind farm yield assessment often relies on analytical wake models. Calibrating these models can...
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