This work proposes a novel data-driven model capable of providing accurate predictions for the power generation of all wind turbines in wind farms of arbitrary layout, yaw angle configurations and wind conditions. The proposed model functions by encoding a wind farm into a fully-connected graph and processing the graph representation through a graph transformer. The graph transformer surrogate is shown to generalise well and is able to uncover latent structural patterns within the graph representation of wind farms. It is demonstrated how the resulting surrogate model can be used to optimise yaw angle configurations using genetic algorithms, achieving similar levels of accuracy to industrially-standard wind farm simulation tools while only taking a fraction of the computational cost.

翻译：本文提出了一种新颖的数据驱动模型，能够为任意布局、偏航角配置和风况下的风电场中所有风力发电机的发电量提供精确预测。该模型通过将风电场编码为全连接图，并利用图变换器处理图表示来实现功能。研究表明，图变换器代理模型具有良好的泛化能力，并能揭示风电场图表示中的潜在结构模式。本文进一步展示了如何利用遗传算法优化偏航角配置，该代理模型在达到与工业标准风电场仿真工具相近精度的同时，仅需极小的计算成本。