Articles | Volume 3, issue 1
Wind Energ. Sci., 3, 395–408, 2018
https://doi.org/10.5194/wes-3-395-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Wind Energ. Sci., 3, 395–408, 2018
https://doi.org/10.5194/wes-3-395-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article 14 Jun 2018
Research article | 14 Jun 2018
Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data
Niko Mittelmeier and Martin Kühn
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Cited
12 citations as recorded by crossref.
- How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine P. Murphy et al. 10.5194/wes-5-1169-2020
- Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI J. Chatterjee & N. Dethlefs 10.1088/1742-6596/1618/2/022022
- A Data-Mining Compensation Approach for Yaw Misalignment on Wind Turbine Y. Bao & Q. Yang 10.1109/TII.2021.3065702
- Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
- Large-eddy simulation of a utility-scale wind farm in complex terrain X. Yang et al. 10.1016/j.apenergy.2018.08.049
- Robust active wake control in consideration of wind direction variability and uncertainty A. Rott et al. 10.5194/wes-3-869-2018
- Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications R. Bakhshi & P. Sandborn 10.1002/we.2493
- Detection of wakes in the inflow of turbines using nacelle lidars D. Held & J. Mann 10.5194/wes-4-407-2019
- Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations P. Hulsman et al. 10.5194/wes-5-309-2020
- Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network Z. Lin & X. Liu 10.1016/j.energy.2020.117693
- IEA Wind Task 32 and Task 37: Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering E. Simley et al. 10.1088/1742-6596/1618/4/042029
- Exploring the complexities associated with full-scale wind plant wake mitigation control experiments J. Duncan Jr. et al. 10.5194/wes-5-469-2020
12 citations as recorded by crossref.
- How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine P. Murphy et al. 10.5194/wes-5-1169-2020
- Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI J. Chatterjee & N. Dethlefs 10.1088/1742-6596/1618/2/022022
- A Data-Mining Compensation Approach for Yaw Misalignment on Wind Turbine Y. Bao & Q. Yang 10.1109/TII.2021.3065702
- Wake steering optimization under uncertainty J. Quick et al. 10.5194/wes-5-413-2020
- Large-eddy simulation of a utility-scale wind farm in complex terrain X. Yang et al. 10.1016/j.apenergy.2018.08.049
- Robust active wake control in consideration of wind direction variability and uncertainty A. Rott et al. 10.5194/wes-3-869-2018
- Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications R. Bakhshi & P. Sandborn 10.1002/we.2493
- Detection of wakes in the inflow of turbines using nacelle lidars D. Held & J. Mann 10.5194/wes-4-407-2019
- Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations P. Hulsman et al. 10.5194/wes-5-309-2020
- Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network Z. Lin & X. Liu 10.1016/j.energy.2020.117693
- IEA Wind Task 32 and Task 37: Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering E. Simley et al. 10.1088/1742-6596/1618/4/042029
- Exploring the complexities associated with full-scale wind plant wake mitigation control experiments J. Duncan Jr. et al. 10.5194/wes-5-469-2020
Latest update: 20 Jan 2022
Short summary
Upwind horizontal axis wind turbines need to be aligned with the main wind direction to maximize energy yield. This paper presents new methods to improve turbine alignment and detect changes during operational lifetime with standard nacelle met mast instruments. The flow distortion behind the rotor is corrected with a multilinear regression model and two alignment changes are detected with an accuracy of ±1.4° within 3 days of operation after the change is introduced.
Upwind horizontal axis wind turbines need to be aligned with the main wind direction to maximize...
