Articles | Volume 8, issue 8
https://doi.org/10.5194/wes-8-1235-2023
© Author(s) 2023. 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-8-1235-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Aug 2023
Research article |
| 01 Aug 2023
| 01 Aug 2023
Stochastic gradient descent for wind farm optimization
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Pierre-Elouan Rethore
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Mads Mølgaard Pedersen
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Rafael Valotta Rodrigues
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Mikkel Friis-Møller
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
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Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Wind turbine positions are often optimized to avoid wake losses. These losses depend on...
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