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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Total article views: 6,851 (including HTML, PDF, and XML)
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- A Review of Diagnostic Methods for Yaw Errors in Horizontal Axis Wind Turbines Q. Li et al. https://doi.org/10.3390/en18030588
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- Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. https://doi.org/10.1002/we.2799
- Condition Monitoring and Fault Diagnosis of Wind Turbine: A Systematic Literature Review M. Hussain et al. https://doi.org/10.1109/ACCESS.2024.3514747
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- A concept for retraining a data-driven wind condition estimation model on a new wind turbine system J. Stegink et al. https://doi.org/10.1088/1742-6596/3224/9/092022
- Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications R. Bakhshi & P. Sandborn https://doi.org/10.1002/we.2493
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- Detection of wakes in the inflow of turbines using nacelle lidars D. Held & J. Mann https://doi.org/10.5194/wes-4-407-2019
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32 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. https://doi.org/10.5194/wes-5-1169-2020
- A Data-Mining Compensation Approach for Yaw Misalignment on Wind Turbine Y. Bao & Q. Yang https://doi.org/10.1109/TII.2021.3065702
- Wake steering optimization under uncertainty J. Quick et al. https://doi.org/10.5194/wes-5-413-2020
- Horizontal axis wind turbine yaw differential error reduction approach E. Solomin et al. https://doi.org/10.1016/j.enconman.2022.115255
- Individuation of Wind Turbine Systematic Yaw Error through SCADA Data D. Astolfi et al. https://doi.org/10.3390/en15218165
- A General Method For The Diagnosis Of Wind Turbine Systematic Yaw Error Based Solely On SCADA Data D. Astolfi et al. https://doi.org/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. https://doi.org/10.3390/en17246381
- Economic optimal control of source-storage collaboration based on wind power forecasting for transient frequency regulation L. Yao et al. https://doi.org/10.1016/j.est.2024.111002
- Diagnosis of wind turbine systematic yaw error through nacelle anemometer measurement analysis D. Astolfi et al. https://doi.org/10.1016/j.segan.2023.101071
- Towards a Unified SCADA-Based Workflow for Wind Turbine Performance Analysis and Static Errors Diagnosis D. Astolfi et al. https://doi.org/10.1088/1742-6596/3224/6/062015
- Robust active wake control in consideration of wind direction variability and uncertainty A. Rott et al. https://doi.org/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. https://doi.org/10.1016/j.enconman.2022.115786
- Improving the Fluid–Structure Interaction for Effective Energy Harvesting in Vertical-Axis Wind Walls with Symmetrical Configuration P. Loktongbam et al. https://doi.org/10.3390/sym17122115
- Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations P. Hulsman et al. https://doi.org/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. https://doi.org/10.1088/1742-6596/1618/4/042029
- A Review of Diagnostic Methods for Yaw Errors in Horizontal Axis Wind Turbines Q. Li et al. https://doi.org/10.3390/en18030588
- Condition Monitoring of Wind Turbine Systems by Explainable Artificial Intelligence Techniques D. Astolfi et al. https://doi.org/10.3390/s23125376
- Exploring the complexities associated with full-scale wind plant wake mitigation control experiments J. Duncan Jr. et al. https://doi.org/10.5194/wes-5-469-2020
- On Wind Directions Estimated by Nacelle Lidar Under Different Reconstruction Methods F. Guo et al. https://doi.org/10.1002/we.70098
- Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI J. Chatterjee & N. Dethlefs https://doi.org/10.1088/1742-6596/1618/2/022022
- Observer-based power forecast of individual and aggregated offshore wind turbines F. Theuer et al. https://doi.org/10.5194/wes-7-2099-2022
- Investigation of Wind Turbine Static Yaw Error Based on Utility-Scale Controlled Experiments D. Astolfi et al. https://doi.org/10.1109/TIA.2024.3397956
- Large-eddy simulation of a utility-scale wind farm in complex terrain X. Yang et al. https://doi.org/10.1016/j.apenergy.2018.08.049
- AI-Based Wind Tracking and Yaw Control System for Optimizing Wind Turbine Efficiency S. Mahmud et al. https://doi.org/10.3390/pr14071084
- Wind plant power maximization via extremum seeking yaw control: A wind tunnel experiment D. Kumar et al. https://doi.org/10.1002/we.2799
- Condition Monitoring and Fault Diagnosis of Wind Turbine: A Systematic Literature Review M. Hussain et al. https://doi.org/10.1109/ACCESS.2024.3514747
- Accounting for Environmental Conditions in Data-Driven Wind Turbine Power Models R. Pandit et al. https://doi.org/10.1109/TSTE.2022.3204453
- A concept for retraining a data-driven wind condition estimation model on a new wind turbine system J. Stegink et al. https://doi.org/10.1088/1742-6596/3224/9/092022
- Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications R. Bakhshi & P. Sandborn https://doi.org/10.1002/we.2493
- Wind vane correction during yaw misalignment for horizontal-axis wind turbines A. Rott et al. https://doi.org/10.5194/wes-8-1755-2023
- Detection of wakes in the inflow of turbines using nacelle lidars D. Held & J. Mann https://doi.org/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 https://doi.org/10.1016/j.energy.2020.117693
Saved (final revised paper)
Latest update: 01 Aug 2026
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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