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
Viewed
Total article views: 2,666 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Aug 2021)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
1,848 | 755 | 63 | 2,666 | 59 | 47 |
- HTML: 1,848
- PDF: 755
- XML: 63
- Total: 2,666
- BibTeX: 59
- EndNote: 47
Total article views: 1,723 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Feb 2022)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
1,255 | 433 | 35 | 1,723 | 54 | 40 |
- HTML: 1,255
- PDF: 433
- XML: 35
- Total: 1,723
- BibTeX: 54
- EndNote: 40
Total article views: 943 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Aug 2021)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
593 | 322 | 28 | 943 | 5 | 7 |
- HTML: 593
- PDF: 322
- XML: 28
- Total: 943
- BibTeX: 5
- EndNote: 7
Viewed (geographical distribution)
Total article views: 2,666 (including HTML, PDF, and XML)
Thereof 2,542 with geography defined
and 124 with unknown origin.
Total article views: 1,723 (including HTML, PDF, and XML)
Thereof 1,642 with geography defined
and 81 with unknown origin.
Total article views: 943 (including HTML, PDF, and XML)
Thereof 900 with geography defined
and 43 with unknown origin.
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Cited
15 citations as recorded by crossref.
- Estimation of Hub Center Loads for Individual Pitch Control for Wind Turbines Based on Tower Loads and Machine Learning S. Kiyoki et al. 10.3390/electronics13183648
- Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model J. Bilbao et al. 10.1017/dce.2022.38
- Farm‐wide interface fatigue loads estimation: A data‐driven approach based on accelerometers F. de N Santos et al. 10.1002/we.2888
- Efficient fatigue damage estimation of offshore wind turbine foundation under wind-wave actions T. Li et al. 10.1016/j.jcsr.2024.108903
- A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method C. Feng et al. 10.1002/adem.202201430
- Machine Learning-Based Prediction of 2 MW Wind Turbine Tower Loads During Power Production Based on Nacelle Behavior S. Kiyoki et al. 10.3390/en18010216
- Probabilistic temporal extrapolation of fatigue damage of offshore wind turbine substructures based on strain measurements C. Hübler & R. Rolfes 10.5194/wes-7-1919-2022
- Power data integrity verification method based on chameleon authentication tree algorithm and missing tendency value X. Liu et al. 10.1515/ehs-2023-0067
- Scour assessment for offshore wind turbines: a state-of-the-art review X. Feng et al. 10.1007/s13349-025-00934-w
- Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks N. Hlaing et al. 10.1177/14759217231186048
- Predictions for Bending Strain at the Tower Bottom of Offshore Wind Turbine Based on the LSTM Model S. Lee et al. 10.3390/en16134922
- An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures Y. Yang et al. 10.3390/jmse12030377
- Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts M. Masoumi 10.3390/jmse11101855
- Flexible multi-fidelity framework for load estimation of wind farms through graph neural networks and transfer learning G. Duthé et al. 10.1017/dce.2024.35
- Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks F. de N Santos et al. 10.1016/j.renene.2023.01.093
15 citations as recorded by crossref.
- Estimation of Hub Center Loads for Individual Pitch Control for Wind Turbines Based on Tower Loads and Machine Learning S. Kiyoki et al. 10.3390/electronics13183648
- Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model J. Bilbao et al. 10.1017/dce.2022.38
- Farm‐wide interface fatigue loads estimation: A data‐driven approach based on accelerometers F. de N Santos et al. 10.1002/we.2888
- Efficient fatigue damage estimation of offshore wind turbine foundation under wind-wave actions T. Li et al. 10.1016/j.jcsr.2024.108903
- A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method C. Feng et al. 10.1002/adem.202201430
- Machine Learning-Based Prediction of 2 MW Wind Turbine Tower Loads During Power Production Based on Nacelle Behavior S. Kiyoki et al. 10.3390/en18010216
- Probabilistic temporal extrapolation of fatigue damage of offshore wind turbine substructures based on strain measurements C. Hübler & R. Rolfes 10.5194/wes-7-1919-2022
- Power data integrity verification method based on chameleon authentication tree algorithm and missing tendency value X. Liu et al. 10.1515/ehs-2023-0067
- Scour assessment for offshore wind turbines: a state-of-the-art review X. Feng et al. 10.1007/s13349-025-00934-w
- Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks N. Hlaing et al. 10.1177/14759217231186048
- Predictions for Bending Strain at the Tower Bottom of Offshore Wind Turbine Based on the LSTM Model S. Lee et al. 10.3390/en16134922
- An Overview on Structural Health Monitoring and Fault Diagnosis of Offshore Wind Turbine Support Structures Y. Yang et al. 10.3390/jmse12030377
- Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts M. Masoumi 10.3390/jmse11101855
- Flexible multi-fidelity framework for load estimation of wind farms through graph neural networks and transfer learning G. Duthé et al. 10.1017/dce.2024.35
- Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks F. de N Santos et al. 10.1016/j.renene.2023.01.093
Latest update: 01 May 2025
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...
Altmetrics
Final-revised paper
Preprint