Articles | Volume 4, issue 4
https://doi.org/10.5194/wes-4-549-2019
© Author(s) 2019. 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-4-549-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Oct 2019
Research article |
| 11 Oct 2019
| 11 Oct 2019
Validation of a lookup-table approach to modeling turbine fatigue loads in wind farms under active wake control
Hector Mendez Reyes
TNO, ECN-TNO, Westerduinweg 3, 1755 LE Petten, the Netherlands
TNO, ECN-TNO, Westerduinweg 3, 1755 LE Petten, the Netherlands
Bart Doekemeijer
TU Delft, DCSC, Mekelweg 2, 2628 CD Delft, the Netherlands
Jan-Willem van Wingerden
TU Delft, DCSC, Mekelweg 2, 2628 CD Delft, the Netherlands
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Total article views: 2,380 (including HTML, PDF, and XML)
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Cited
24 citations as recorded by crossref.
- Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines J. Sun et al. 10.1016/j.renene.2022.08.137
- Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
- A hierarchical supervisory wind power plant controller K. Merz et al. 10.1088/1742-6596/2018/1/012026
- A Machine Learning Method for Modeling Wind Farm Fatigue Load Y. Miao et al. 10.3390/app12157392
- Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm G. Gasparis et al. 10.3390/en13236360
- A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control R. He et al. 10.1016/j.apenergy.2022.120013
- From wind conditions to operational strategy: optimal planning of wind turbine damage progression over its lifetime N. Requate et al. 10.5194/wes-8-1727-2023
- A digital twin solution for floating offshore wind turbines validated using a full-scale prototype E. Branlard et al. 10.5194/wes-9-1-2024
- Quantitative assessment on fatigue damage induced by wake effect and yaw misalignment for floating offshore wind turbines T. Tao et al. 10.1016/j.oceaneng.2023.116004
- Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations E. Branlard et al. 10.5194/wes-5-1155-2020
- An initial study into the potential of wind farm control to reduce fatigue loads and extend asset life M. Harrison et al. 10.1088/1742-6596/1618/2/022007
- Integrating preventive and predictive maintenance policies with system dynamics: A decision table approach G. Bilal Yıldız & B. Soylu 10.1016/j.aei.2023.101952
- Data‐driven modeling for fatigue loads of large‐scale wind turbines under active power regulation J. Yang et al. 10.1002/we.2589
- Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control R. He et al. 10.1016/j.apenergy.2023.120878
- Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control M. Lin & F. Porté-Agel 10.3390/en16062542
- Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020
- Wake steering strategies for combined power increase and fatigue damage mitigation: an LES study B. López et al. 10.1088/1742-6596/1618/2/022067
- A model to calculate fatigue damage caused by partial waking during wind farm optimization A. Stanley et al. 10.5194/wes-7-433-2022
- Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine A. Croce et al. 10.5194/wes-7-1-2022
- Predicting wind farm wake losses with deep convolutional hierarchical encoder–decoder neural networks D. Romero et al. 10.1063/5.0168973
- Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm T. Tao et al. 10.1016/j.oceaneng.2024.117706
- T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case C. Galinos et al. 10.3390/en13061306
- Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions B. Doekemeijer et al. 10.1016/j.renene.2020.04.007
- Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 P. Fleming et al. 10.5194/wes-5-945-2020
24 citations as recorded by crossref.
- Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines J. Sun et al. 10.1016/j.renene.2022.08.137
- Turbulence and Control of Wind Farms C. Shapiro et al. 10.1146/annurev-control-070221-114032
- A hierarchical supervisory wind power plant controller K. Merz et al. 10.1088/1742-6596/2018/1/012026
- A Machine Learning Method for Modeling Wind Farm Fatigue Load Y. Miao et al. 10.3390/app12157392
- Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm G. Gasparis et al. 10.3390/en13236360
- A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control R. He et al. 10.1016/j.apenergy.2022.120013
- From wind conditions to operational strategy: optimal planning of wind turbine damage progression over its lifetime N. Requate et al. 10.5194/wes-8-1727-2023
- A digital twin solution for floating offshore wind turbines validated using a full-scale prototype E. Branlard et al. 10.5194/wes-9-1-2024
- Quantitative assessment on fatigue damage induced by wake effect and yaw misalignment for floating offshore wind turbines T. Tao et al. 10.1016/j.oceaneng.2023.116004
- Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations E. Branlard et al. 10.5194/wes-5-1155-2020
- An initial study into the potential of wind farm control to reduce fatigue loads and extend asset life M. Harrison et al. 10.1088/1742-6596/1618/2/022007
- Integrating preventive and predictive maintenance policies with system dynamics: A decision table approach G. Bilal Yıldız & B. Soylu 10.1016/j.aei.2023.101952
- Data‐driven modeling for fatigue loads of large‐scale wind turbines under active power regulation J. Yang et al. 10.1002/we.2589
- Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control R. He et al. 10.1016/j.apenergy.2023.120878
- Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control M. Lin & F. Porté-Agel 10.3390/en16062542
- Design and analysis of a wake steering controller with wind direction variability E. Simley et al. 10.5194/wes-5-451-2020
- Wake steering strategies for combined power increase and fatigue damage mitigation: an LES study B. López et al. 10.1088/1742-6596/1618/2/022067
- A model to calculate fatigue damage caused by partial waking during wind farm optimization A. Stanley et al. 10.5194/wes-7-433-2022
- Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine A. Croce et al. 10.5194/wes-7-1-2022
- Predicting wind farm wake losses with deep convolutional hierarchical encoder–decoder neural networks D. Romero et al. 10.1063/5.0168973
- Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm T. Tao et al. 10.1016/j.oceaneng.2024.117706
- T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case C. Galinos et al. 10.3390/en13061306
- Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions B. Doekemeijer et al. 10.1016/j.renene.2020.04.007
- Continued results from a field campaign of wake steering applied at a commercial wind farm – Part 2 P. Fleming et al. 10.5194/wes-5-945-2020
Latest update: 18 Apr 2024
Short summary
Within wind farms, the wind turbines interact with each other through their wakes. Turbines operating in these wakes have lower power production and increased wear and tear. Wake redirection is control strategy to steer the wakes aside from downstream turbines, increasing the power yield of the farm. Models for predicting the power gain and impacts on wear exist, but they are still immature and require validation. The validation of such a model is the purpose of this paper.
Within wind farms, the wind turbines interact with each other through their wakes. Turbines...
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