论文标题
通过大型涡流模拟和高斯流程回归预测唤醒转向的唤醒转向的好处
Predicting the benefit of wake steering on the annual energy production of a wind farm using large eddy simulations and Gaussian process regression
论文作者
论文摘要
近年来，唤醒转向已被建立为一种有前途的方法，以增加风电场的能源产量。当前估计唤醒转向年度能源产量（AEP）的实践包括通过简化的替代模型评估风电场，对估计收益产生了巨大的不确定性。本文提出了一个框架，以确定在AEP上唤醒转向的好处，并结合了替代模型和大型涡流模拟的仿真结果，以减少不确定性。此外，考虑到随时随地的风向，以更好地表示真实风电场的环境条件。高斯过程回归用于将两个数据集结合到单个改进的能量增益模型中。该模型估计，与替代模型的估计值相比，该模型的AEP估计AEP增长了0.60％。
In recent years, wake steering has been established as a promising method to increase the energy yield of a wind farm. Current practice in estimating the benefit of wake steering on the annual energy production (AEP) consists of evaluating the wind farm with simplified surrogate models, casting a large uncertainty on the estimated benefit. This paper presents a framework for determining the benefit of wake steering on the AEP, incorporating simulation results from a surrogate model and large eddy simulations in order to reduce the uncertainty. Furthermore, a time-varying wind direction is considered for a better representation of the ambient conditions at the real wind farm site. Gaussian process regression is used to combine the two data sets into a single improved model of the energy gain. This model estimates a 0.60% gain in AEP for the considered wind farm, which is a 76% increase compared to the estimate of the surrogate model.
