2022
DOI: 10.5194/wes-2022-104
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Stochastic Gradient Descent for Wind Farm Optimization

Abstract: Abstract. It is important to optimize wind turbine positions to mitigate potential wake losses. To perform this optimization, atmospheric conditions, such as the inflow speed and direction, are assigned probability distributions according to measured data, and these conditions are propagated through engineering wake models to estimate the annual energy production (AEP). This study presents stochastic gradient descent (SGD) for wind farm optimization, which is an approach that estimates the gradient of the AEP … Show more

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Cited by 3 publications
(3 citation statements)
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“…Data availability. The data used in this study have been made available on Zenodo: https://doi.org/10.5281/zenodo.8202150 (Quick, 2023).…”
Section: Discussionmentioning
confidence: 99%
“…Data availability. The data used in this study have been made available on Zenodo: https://doi.org/10.5281/zenodo.8202150 (Quick, 2023).…”
Section: Discussionmentioning
confidence: 99%
“…In [8], triangulation is used as the starting point of the gradient method, with the additional idea of maximizing turbine dispersion, thus reducing maximum turbine loads. Quick et al in [17], uses a stochastic gradient descent, estimates the gradient of the AEP using Monte Carlo simulation, achieving lower computation times as long as the farm size is above 225 turbines.…”
Section: Introductionmentioning
confidence: 99%
“…For the AEP calculation and the wake modeling involved in the simulations, TOPFARM relies in PyWake (Pedersen et al, 2019), another DTU open source Python library that offers fast AEP evaluation from a range of engineering wake models. Recent works using TOPFARM (Ciavarra et al, 2022;Riva et al, 2020), and PyWake (Rodrigues et al, 2022;Fischereit et al, 2021;Forsting et al, 2021;Pedersen et al, 2021;Quick et al, 2022) can be found in the literature.…”
mentioning
confidence: 99%