Wind Energy Science
Wind Energy Science
WES
Articles | Volume 7, issue 1
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.
https://doi.org/10.5194/wes-7-299-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 7, issue 1
Research article
 | 
08 Feb 2022
Research article |  | 08 Feb 2022

Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups

Francisco d N Santos, Nymfa Noppe, Wout Weijtjens, and Christof Devriendt

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Cited articles

Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., and Ridella, S.: The `K' in K-fold Cross Validation, in: ESANN, 441–446, https://www.esann.org/sites/default/files/proceedings/legacy/es2012-62.pdf (last access: 7 February 2022), 2012. a
Antoniadou, I., Dervilis, N., Papatheou, E., Maguire, A., and Worden, K.: Aspects of structural health and condition monitoring of offshore wind turbines, Philos. T. Roy. Soc. A, 373, 20140075, https://doi.org/10.1098/rsta.2014.0075, 2015. a
Avendaño-Valencia, L. D., Abdallah, I., and Chatzi, E.: Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression, Renew. Energ., 170, 539–561, 2021. a, b, c
Baudisch, R.: Structural health monitoring of offshore wind turbines, MS thesis, Danmarks Tekniske Universitet, 2012. a, b
Bishop, C. M.: Pattern recognition and machine learning, Springer, ISBN 978-0-387-31073-2, 2006. a
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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.
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