  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.
