Articles | Volume 8, issue 8
https://doi.org/10.5194/wes-8-1235-2023
https://doi.org/10.5194/wes-8-1235-2023
Research article
 | 
01 Aug 2023
Research article |  | 01 Aug 2023

Stochastic gradient descent for wind farm optimization

Julian Quick, Pierre-Elouan Rethore, Mads Mølgaard Pedersen, Rafael Valotta Rodrigues, and Mikkel Friis-Møller

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Latest update: 15 Sep 2024
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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.
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