Learning to Optimise Wind Farms with Graph Transformers
Siyi Li Arnaud Robert A. Aldo Faisal Matthew D. Piggott
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.