Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions, and the interaction between wakes. Physics-based models that capture the wake flow-field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional \emph{latent} space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow-fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.
翻译:风力农场的设计主要取决于风轮机回转流向大气风条件的变异性,以及休眠之间的相互作用。基于物理的模型在捕捉后回流场和高纤维度的物理模型上计算成本很高,以进行风力农场的优化布局,因此,数据驱动的减序模型可以代表模拟风力农场的有效替代方法。在这项工作中,我们使用现实世界光探测和测距(LiDAR)测距来利用机器学习来构建预测性代孕模型。具体地说,我们首先展示了利用深神经网络,使用深地球光探测和测距测距测距仪测量模型来构建预测性代孕期模型模型。我们首先展示了深度自动测距模型的深度测距模型来寻找低维度流空间图,然后在测距后测距测距测距测距测距测距测距测量过程中,我们用高清晰度测距测距模型的测距流程, 最终展示了我们测距和测距的测距流数据, 提供我们测距和测距数据测距的测距的测程的测程数据, 提供我们测程的测程的测程的测程的测程的测程的测程数据, 向数据, 提供我们测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程数据,以高数据的测程数据的测程的测程的测程的测程数据的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程的测程数据的测程的测程的测程的测程的测程数据的测程的测程数据的测程的测程数据的测程。。