End-to-end Wind Turbine Wake Modelling with Deep Graph Representation Learning

TL;DR

This paper introduces a deep graph neural network model for accurate, fast, and generalizable wind turbine wake prediction directly on unstructured meshes, validated with high-fidelity data and real-world case studies.

Contribution

It presents a novel end-to-end graph neural network approach for wind wake modeling that outperforms traditional methods in accuracy and generalization, applicable to CFD simulations.

Findings

Accurately predicts 3D flow fields under various conditions.

Demonstrates good generalization to unseen data.

Successfully applied to real wind farm power prediction.

Abstract

Wind turbine wake modelling is of crucial importance to accurate resource assessment, to layout optimisation, and to the operational control of wind farms. This work proposes a surrogate model for the representation of wind turbine wakes based on a state-of-the-art graph representation learning method termed a graph neural network. The proposed end-to-end deep learning model operates directly on unstructured meshes and has been validated against high-fidelity data, demonstrating its ability to rapidly make accurate 3D flow field predictions for various inlet conditions and turbine yaw angles. The specific graph neural network model employed here is shown to generalise well to unseen data and is less sensitive to over-smoothing compared to common graph neural networks. A case study based upon a real world wind farm further demonstrates the capability of the proposed approach to predict farm scale power generation. Moreover, the proposed graph neural network framework is flexible and highly generic and as formulated here can be applied to any steady state computational fluid dynamics simulations on unstructured meshes.