Articles | Volume 7, issue 3
https://doi.org/10.5194/wes-7-1069-2022
© Author(s) 2022. 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-7-1069-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 May 2022
Research article |
| 24 May 2022
| 24 May 2022
Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Kurt Schaldemose Hansen
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Xiaoli Guo Larsén
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Maarten Paul van der Laan
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Pierre-Elouan Réthoré
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Juan Pablo Murcia Leon
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
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22 citations as recorded by crossref.
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- The future of offshore wind power production: Wake and climate impacts S. Warder & M. Piggott 10.1016/j.apenergy.2024.124956
- Wind turbine wakes modeling and applications: Past, present, and future L. Wang et al. 10.1016/j.oceaneng.2024.118508
- Mesoscale impact of the sea surface on the performance of offshore wind farms C. Wu et al. 10.1016/j.jclepro.2022.133741
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- Can mesoscale models capture the effect from cluster wakes offshore? M. Gomez et al. 10.1088/1742-6596/2767/6/062013
- Evaluation of Engineering Models for Large‐Scale Cluster Wakes With the Help of In Situ Airborne Measurements K. zum Berge et al. 10.1002/we.2942
- On the accuracy of predicting wind-farm blockage A. Meyer Forsting et al. 10.1016/j.renene.2023.05.129
- Gradient-based wind farm layout optimization with inclusion and exclusion zones J. Criado Risco et al. 10.5194/wes-9-585-2024
- Flexible multi-fidelity framework for load estimation of wind farms through graph neural networks and transfer learning G. Duthé et al. 10.1017/dce.2024.35
- An investigation of spatial wind direction variability and its consideration in engineering models A. von Brandis et al. 10.5194/wes-8-589-2023
- Simulating wake losses of the Danish Energy Island wind farm cluster M. van der Laan et al. 10.1088/1742-6596/2505/1/012015
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- Learning to optimise wind farms with graph transformers S. Li et al. 10.1016/j.apenergy.2024.122758
- A coupled turbine-interaction wind farm parameterization in the Weather Research and Forecasting model C. Wu et al. 10.1016/j.enconman.2023.116919
- Power Production, Inter- and Intra-Array Wake Losses from the U.S. East Coast Offshore Wind Energy Lease Areas S. Pryor & R. Barthelmie 10.3390/en17051063
2 citations as recorded by crossref.
Latest update: 13 Dec 2024
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Short summary
Wind turbines extract kinetic energy from the flow to create electricity. This induces a wake of reduced wind speed downstream of a turbine and consequently downstream of a wind farm. Different types of numerical models have been developed to calculate this effect. In this study, we compared models of different complexity, together with measurements over two wind farms. We found that higher-fidelity models perform better and the considered rapid models cannot fully capture the wake effect.
Wind turbines extract kinetic energy from the flow to create electricity. This induces a wake of...
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