Articles | Volume 3, issue 1
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.
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
| 14 Jun 2018
Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data
Niko Mittelmeier
CORRESPONDING AUTHOR
Senvion GmbH, Überseering 10, 22297 Hamburg, Germany
Martin Kühn
ForWind – University of Oldenburg, Institute of Physics,
Küpkersweg 70, 26129 Oldenburg, Germany
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- An improved data-driven methodology and field-test verification of yaw misalignment calibration on wind turbines C. Qu et al. 10.1016/j.enconman.2022.115786
- 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
- 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
- A Review of Diagnostic Methods for Yaw Errors in Horizontal Axis Wind Turbines Q. Li et al. 10.3390/en18030588
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- Exploring the complexities associated with full-scale wind plant wake mitigation control experiments J. Duncan Jr. et al. 10.5194/wes-5-469-2020
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- Large-eddy simulation of a utility-scale wind farm in complex terrain X. Yang et al. 10.1016/j.apenergy.2018.08.049
- Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. 10.1002/we.2799
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- Accounting for Environmental Conditions in Data-Driven Wind Turbine Power Models R. Pandit et al. 10.1109/TSTE.2022.3204453
- Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications R. Bakhshi & P. Sandborn 10.1002/we.2493
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- Detection of wakes in the inflow of turbines using nacelle lidars D. Held & J. Mann 10.5194/wes-4-407-2019
- 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
27 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
- 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
- Horizontal axis wind turbine yaw differential error reduction approach E. Solomin et al. 10.1016/j.enconman.2022.115255
- Individuation of Wind Turbine Systematic Yaw Error through SCADA Data D. Astolfi et al. 10.3390/en15218165
- A General Method For The Diagnosis Of Wind Turbine Systematic Yaw Error Based Solely On SCADA Data D. Astolfi et al. 10.1088/1742-6596/2767/4/042007
- Wind Turbine Static Errors Related to Yaw, Pitch or Anemometer Apparatus: Guidelines for the Diagnosis and Related Performance Assessment D. Astolfi et al. 10.3390/en17246381
- Economic optimal control of source-storage collaboration based on wind power forecasting for transient frequency regulation L. Yao et al. 10.1016/j.est.2024.111002
- Diagnosis of wind turbine systematic yaw error through nacelle anemometer measurement analysis D. Astolfi et al. 10.1016/j.segan.2023.101071
- Robust active wake control in consideration of wind direction variability and uncertainty A. Rott et al. 10.5194/wes-3-869-2018
- An improved data-driven methodology and field-test verification of yaw misalignment calibration on wind turbines C. Qu et al. 10.1016/j.enconman.2022.115786
- 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
- 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
- A Review of Diagnostic Methods for Yaw Errors in Horizontal Axis Wind Turbines Q. Li et al. 10.3390/en18030588
- Condition Monitoring of Wind Turbine Systems by Explainable Artificial Intelligence Techniques D. Astolfi et al. 10.3390/s23125376
- Exploring the complexities associated with full-scale wind plant wake mitigation control experiments J. Duncan Jr. et al. 10.5194/wes-5-469-2020
- 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
- Observer-based power forecast of individual and aggregated offshore wind turbines F. Theuer et al. 10.5194/wes-7-2099-2022
- Investigation of Wind Turbine Static Yaw Error Based on Utility-Scale Controlled Experiments D. Astolfi et al. 10.1109/TIA.2024.3397956
- Large-eddy simulation of a utility-scale wind farm in complex terrain X. Yang et al. 10.1016/j.apenergy.2018.08.049
- Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. 10.1002/we.2799
- Condition Monitoring and Fault Diagnosis of Wind Turbine: A Systematic Literature Review M. Hussain et al. 10.1109/ACCESS.2024.3514747
- Accounting for Environmental Conditions in Data-Driven Wind Turbine Power Models R. Pandit et al. 10.1109/TSTE.2022.3204453
- Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications R. Bakhshi & P. Sandborn 10.1002/we.2493
- Wind vane correction during yaw misalignment for horizontal-axis wind turbines A. Rott et al. 10.5194/wes-8-1755-2023
- Detection of wakes in the inflow of turbines using nacelle lidars D. Held & J. Mann 10.5194/wes-4-407-2019
- 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
Latest update: 11 Jul 2025
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...
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