Wind farm modelling has been an area of rapidly increasing interest with numerous analytical as well as computational-based approaches developed to extend the margins of wind farm efficiency and maximise power production. In this work, we present the novel ML framework WakeNet, which can reproduce generalised 2D turbine wake velocity fields at hub-height over a wide range of yaw angles, wind speeds and turbulence intensities (TIs), with a mean accuracy of 99.8% compared to the solution calculated using the state-of-the-art wind farm modelling software FLORIS. As the generation of sufficient high-fidelity data for network training purposes can be cost-prohibitive, the utility of multi-fidelity transfer learning has also been investigated. Specifically, a network pre-trained on the low-fidelity Gaussian wake model is fine-tuned in order to obtain accurate wake results for the mid-fidelity Curl wake model. The robustness and overall performance of WakeNet on various wake steering control and layout optimisation scenarios has been validated through power-gain heatmaps, obtaining at least 90% of the power gained through optimisation performed with FLORIS directly. We also demonstrate that when utilising the Curl model, WakeNet is able to provide similar power gains to FLORIS, two orders of magnitude faster (e.g. 10 minutes vs 36 hours per optimisation case). The wake evaluation time of wakeNet when trained on a high-fidelity CFD dataset is expected to be similar, thus further increasing computational time gains. These promising results show that generalised wake modelling with ML tools can be accurate enough to contribute towards active yaw and layout optimisation, while producing realistic optimised configurations at a fraction of the computational cost, hence making it feasible to perform real-time active yaw control as well as robust optimisation under uncertainty.

翻译：风电场建模是一个快速增长的研究领域，目前已发展出多种解析和计算方法以提升风电场效率并最大化发电量。本文提出了一种新型机器学习框架WakeNet，该框架能够生成通用化的二维涡轮尾流速度场（轮毂高度处），覆盖较宽的偏航角、风速和湍流强度范围，与使用先进风电场建模软件FLORIS计算的解相比，平均精度达99.8%。由于为网络训练生成足够的高保真数据成本高昂，本文还研究了多保真度迁移学习的效用。具体而言，将基于低保真度高斯尾流模型预训练的网络进行微调，以获得中保真度卷曲尾流模型的精确尾流结果。通过功率增益热力图验证了WakeNet在多种尾流偏航控制和布局优化场景中的鲁棒性与整体性能，其可达到直接使用FLORIS进行优化所得功率增益的至少90%。我们还证明，在使用卷曲模型时，WakeNet能够以快两个数量级的速度（例如每个优化案例10分钟对比36小时）提供与FLORIS相似的功率增益。当WakeNet基于高保真度CFD数据集训练时，其尾流评估时间预计保持相似，从而进一步增加计算时间效益。这些成果表明，基于机器学习工具的通用化尾流建模足以满足主动偏航与布局优化的精度要求，同时能以极低计算成本生成接近真实的优化配置，从而为实时主动偏航控制及不确定性下的稳健优化提供可行方案。