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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Cited articles

Alibrahim, H. and Ludwig, S. A.: Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization, in: 2021 IEEE Congress on Evolutionary Computation (CEC), 28 June–1 July 2021, Kraków, Poland, 1551–1559, https://doi.org/10.1109/CEC45853.2021.9504761, 2021. a
Allen, J., King, R., and Barter, G.: Wind farm simulation and layout optimization in complex terrain, J. Phys.: Conf. Ser., 1452, 012066, https://doi.org/10.1088/1742-6596/1452/1/012066, 2020. a, b
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Baker, N. F., Stanley, A. P., Thomas, J. J., Ning, A., and Dykes, K.: Best practices for wake model and optimization algorithm selection in wind farm layout optimization, in: AIAA Scitech 2019 forum, 7–11 January 2019, San Diego, California, USA, p. 0540, https://doi.org/10.2514/6.2019-0540, 2019. a
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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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