Articles | Volume 3, issue 1
https://doi.org/10.5194/wes-3-139-2018
© Author(s) 2018. 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-3-139-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Mar 2018
Research article |
| 22 Mar 2018
| 22 Mar 2018
Modeling of quasi-static thrust load of wind turbines based on 1 s SCADA data
Nymfa Noppe
CORRESPONDING AUTHOR
Offshore wind infrastructure lab (OWI-lab), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Wout Weijtjens
Offshore wind infrastructure lab (OWI-lab), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Christof Devriendt
Offshore wind infrastructure lab (OWI-lab), Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
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Cited
16 citations as recorded by crossref.
- Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups F. d N Santos et al. 10.5194/wes-7-299-2022
- 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 sensors for wind turbines with machine learning‐based time series models N. Dimitrov & T. Göçmen 10.1002/we.2762
- Angle Calculus-Based Thrust Force Determination on the Blades of a 10 kW Wind Turbine J. Dorrego-Portela et al. 10.3390/technologies12020022
- Optimized identification process of equivalent wind load calculations for offshore wind turbines under standstill conditions X. Dong et al. 10.1016/j.oceaneng.2024.119043
- Impact of calibrated soil-monopile-interaction model on resonance frequencies C. Sastre Jurado et al. 10.1088/1742-6596/2265/3/032098
- Inverse estimation of breaking wave loads on monopile wind turbines K. Maes et al. 10.1016/j.oceaneng.2018.05.049
- Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts M. Masoumi 10.3390/jmse11101855
- SCADA-based neural network thrust load model for fatigue assessment: cross validation with in-situ measurements F. Santos et al. 10.1088/1742-6596/1618/2/022020
- Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. 10.1088/1742-6596/2265/3/032110
- Load identification of a 2.5 MW wind turbine tower using Kalman filtering techniques and BDS data D. Wei et al. 10.1016/j.engstruct.2023.115763
- Wireless High-Resolution Acceleration Measurements for Structural Health Monitoring of Wind Turbine Towers B. Wondra et al. 10.1007/s41688-018-0029-y
- Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks A. Movsessian et al. 10.5194/wes-6-539-2021
- Indirect load measurement method and experimental verification of floating offshore wind turbine X. Feng et al. 10.1016/j.oceaneng.2024.117734
- 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
- Feature Selection Algorithms for Wind Turbine Failure Prediction P. Marti-Puig et al. 10.3390/en12030453
15 citations as recorded by crossref.
- Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups F. d N Santos et al. 10.5194/wes-7-299-2022
- 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 sensors for wind turbines with machine learning‐based time series models N. Dimitrov & T. Göçmen 10.1002/we.2762
- Angle Calculus-Based Thrust Force Determination on the Blades of a 10 kW Wind Turbine J. Dorrego-Portela et al. 10.3390/technologies12020022
- Optimized identification process of equivalent wind load calculations for offshore wind turbines under standstill conditions X. Dong et al. 10.1016/j.oceaneng.2024.119043
- Impact of calibrated soil-monopile-interaction model on resonance frequencies C. Sastre Jurado et al. 10.1088/1742-6596/2265/3/032098
- Inverse estimation of breaking wave loads on monopile wind turbines K. Maes et al. 10.1016/j.oceaneng.2018.05.049
- Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts M. Masoumi 10.3390/jmse11101855
- SCADA-based neural network thrust load model for fatigue assessment: cross validation with in-situ measurements F. Santos et al. 10.1088/1742-6596/1618/2/022020
- Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. 10.1088/1742-6596/2265/3/032110
- Load identification of a 2.5 MW wind turbine tower using Kalman filtering techniques and BDS data D. Wei et al. 10.1016/j.engstruct.2023.115763
- Wireless High-Resolution Acceleration Measurements for Structural Health Monitoring of Wind Turbine Towers B. Wondra et al. 10.1007/s41688-018-0029-y
- Feature selection techniques for modelling tower fatigue loads of a wind turbine with neural networks A. Movsessian et al. 10.5194/wes-6-539-2021
- Indirect load measurement method and experimental verification of floating offshore wind turbine X. Feng et al. 10.1016/j.oceaneng.2024.117734
- 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
1 citations as recorded by crossref.
Latest update: 19 Nov 2024
Short summary
A reliable load history is crucial for a fatigue assessment of wind turbines. However, installing strain sensors to measure the load history on every wind turbine is not economically feasible. In this paper, a technique is proposed to reconstruct the thrust load history of a wind turbine based on high-frequency SCADA data and a trained neural network. Both simulated and real-world results show the potential of high-frequency SCADA for thrust load reconstruction.
A reliable load history is crucial for a fatigue assessment of wind turbines. However,...
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