Articles | Volume 7, issue 1
https://doi.org/10.5194/wes-7-299-2022
© 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-7-299-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups
Francisco d N Santos
CORRESPONDING AUTHOR
OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Nymfa Noppe
OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Wout Weijtjens
OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Christof Devriendt
OWI-Lab, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Related authors
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Ahmed Mujtaba, Wout Weijtjens, Negin Sadeghi, and Christof Devriendt
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-173, https://doi.org/10.5194/wes-2025-173, 2025
Preprint under review for WES
Short summary
Short summary
Our study proposes a random forest based machine learning method for fatigue life prediction of offshore wind turbine monopile foundations compared against traditional approaches, using long-term strain data from turbines in the Belgian North Sea. We found that machine learning predicts fatigue life of monopile foundations more reliably when only short-term measurements are available, while for longer monitoring periods of greater than 12 months, simpler binning methods perform equally well.
Negin Sadeghi, Pietro D'Antuono, Nymfa Noppe, Koen Robbelein, Wout Weijtjens, and Christof Devriendt
Wind Energ. Sci., 8, 1839–1852, https://doi.org/10.5194/wes-8-1839-2023, https://doi.org/10.5194/wes-8-1839-2023, 2023
Short summary
Short summary
Analysis of long-term fatigue damage of four offshore wind turbines using 3 years of measurement data was performed for the first time to gain insight into the low-frequency fatigue damage (LFFD) impact on overall consumed life. The LFFD factor depends on the (linear) stress–life (SN) curve slope, heading, site, signal, and turbine type. Up to ∼ 65 % of the total damage can be related to LFFDs. Therefore, in this case study, the LFFD effect has a significant impact on the final damage.
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Short summary
The estimation of fatigue in offshore wind turbine is highly relevant, as it can help extend the lifetime of these assets. This article attempts to answer this issue by developing a methodology based on artificial intelligence and data collected by sensors installed in real-world turbines. Good results are obtained, and this methodology is further used to learn the value of eight different sensor setups and employed in a real-world wind farm with 48 wind turbines.
The estimation of fatigue in offshore wind turbine is highly relevant, as it can help extend the...
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