Stochastic gradient descent for wind farm optimization

IF 3.6 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
J. Quick, P. Réthoré, M. Mølgaard Pedersen, R. V. Rodrigues, M. Friis-Møller
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引用次数: 2

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, which 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 using Monte Carlo simulation, allowing for the consideration of an arbitrarily large number of atmospheric conditions. SGD is demonstrated using wind farms with square and circular boundaries, considering cases with 100, 144, 225, and 325 turbines, and the results are compared to a deterministic optimization approach. It is shown that SGD finds a larger optimal AEP in substantially less time than the deterministic counterpart as the number of wind turbines is increased.
风电场优化的随机梯度下降
摘要优化风力涡轮机的位置以减轻潜在的尾流损失是很重要的。为了进行这种优化,根据测量数据为大气条件(如流入速度和方向)分配概率分布,这些数据通过工程尾流模型传播,以估计年能源生产(AEP)。本研究提出了用于风电场优化的随机梯度下降(SGD),这是一种使用蒙特卡罗模拟估计AEP梯度的方法,允许考虑任意大量的大气条件。考虑到100、144、225和325台涡轮机的情况,使用具有正方形和圆形边界的风电场对SGD进行了演示,并将结果与确定性优化方法进行了比较。结果表明,随着风力涡轮机数量的增加,SGD在比确定性对应物更短的时间内找到了更大的最优AEP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Wind Energy Science
Wind Energy Science GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
6.90
自引率
27.50%
发文量
115
审稿时长
28 weeks
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