Articles | Volume 5, issue 1
https://doi.org/10.5194/wes-5-199-2020
© Author(s) 2020. 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-5-199-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Feb 2020
Research article |
| 05 Feb 2020
| 05 Feb 2020
The Power Curve Working Group's assessment of wind turbine power performance prediction methods
Joseph C. Y. Lee
CORRESPONDING AUTHOR
National Wind Technology Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
Peter Stuart
Renewable Energy Systems, Kings Langley, Hertfordshire, England, UK
Andrew Clifton
Stuttgart Wind Energy, Institute of Aircraft Design and Manufacture, University of Stuttgart, Stuttgart, Germany
M. Jason Fields
National Wind Technology Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA
Jordan Perr-Sauer
Computational Science Center, National Renewable Energy Laboratory,
Golden, Colorado 80401, USA
Lindy Williams
Computational Science Center, National Renewable Energy Laboratory,
Golden, Colorado 80401, USA
Lee Cameron
Renewable Energy Systems, Kings Langley, Hertfordshire, England, UK
Taylor Geer
DNV GL, Portland, Oregon 97204, USA
Paul Housley
SSE plc, Glasgow, Scotland, UK
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Cited
14 citations as recorded by crossref.
- Grand challenges in the digitalisation of wind energy A. Clifton et al. 10.5194/wes-8-947-2023
- Aerodynamic effects of leading-edge erosion in wind farm flow modeling J. Visbech et al. 10.5194/wes-9-1811-2024
- Validating the next generation of turbine interaction models T. Levick et al. 10.1088/1742-6596/2257/1/012010
- An explainable AI framework for robust and transparent data-driven wind turbine power curve models S. Letzgus & K. Müller 10.1016/j.egyai.2023.100328
- SCADA data analysis for long-term wind turbine performance assessment: A case study D. Astolfi et al. 10.1016/j.seta.2022.102357
- A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset S. Kartal et al. 10.5194/wes-8-1533-2023
- Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern México J. Burgos-Peñaloza et al. 10.3390/en17133323
- Data-Driven wind turbine performance assessment and quantification using SCADA data and field measurements Y. Ding et al. 10.3389/fenrg.2022.1050342
- Wind turbine energy forecasting using real wind farm’s measurement data and performance of gene expression programming analytical model in comparison to traditional algorithms Z. Mehmood & Z. Wang 10.1080/15435075.2024.2415540
- A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging P. Zhang et al. 10.3390/en15134820
- Power curve performance of coastal turbines subject to low turbulence intensity offshore winds Y. Sakagami et al. 10.1007/s40430-022-03942-9
- A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network Z. Luo et al. 10.1109/ACCESS.2020.2994077
- Research challenges and needs for the deployment of wind energy in hilly and mountainous regions A. Clifton et al. 10.5194/wes-7-2231-2022
- Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear D. Kim et al. 10.1016/j.energy.2020.119051
13 citations as recorded by crossref.
- Grand challenges in the digitalisation of wind energy A. Clifton et al. 10.5194/wes-8-947-2023
- Aerodynamic effects of leading-edge erosion in wind farm flow modeling J. Visbech et al. 10.5194/wes-9-1811-2024
- Validating the next generation of turbine interaction models T. Levick et al. 10.1088/1742-6596/2257/1/012010
- An explainable AI framework for robust and transparent data-driven wind turbine power curve models S. Letzgus & K. Müller 10.1016/j.egyai.2023.100328
- SCADA data analysis for long-term wind turbine performance assessment: A case study D. Astolfi et al. 10.1016/j.seta.2022.102357
- A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset S. Kartal et al. 10.5194/wes-8-1533-2023
- Comparative Analysis of Estimated Small Wind Energy Using Different Probability Distributions in a Desert City in Northwestern México J. Burgos-Peñaloza et al. 10.3390/en17133323
- Data-Driven wind turbine performance assessment and quantification using SCADA data and field measurements Y. Ding et al. 10.3389/fenrg.2022.1050342
- Wind turbine energy forecasting using real wind farm’s measurement data and performance of gene expression programming analytical model in comparison to traditional algorithms Z. Mehmood & Z. Wang 10.1080/15435075.2024.2415540
- A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging P. Zhang et al. 10.3390/en15134820
- Power curve performance of coastal turbines subject to low turbulence intensity offshore winds Y. Sakagami et al. 10.1007/s40430-022-03942-9
- A Novel Fault Prediction Method of Wind Turbine Gearbox Based on Pair-Copula Construction and BP Neural Network Z. Luo et al. 10.1109/ACCESS.2020.2994077
- Research challenges and needs for the deployment of wind energy in hilly and mountainous regions A. Clifton et al. 10.5194/wes-7-2231-2022
Latest update: 20 Nov 2024
Short summary
This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
This work summarizes the results of the intelligence-sharing initiative of the Power Curve...
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