Data-driven modelling of turbine wake interactions and flow resistance
in large wind farms
Abstract
Turbine wake and local blockage effects are known to alter wind farm
power production in two different ways: (1) by changing the wind speed
locally in front of each turbine; and (2) by changing the overall flow
resistance in the farm and thus the so-called farm blockage effect. To
better predict these effects with low computational costs, we develop
data-driven emulators of the ‘local’ or ‘internal’ turbine thrust
coefficient CT* as a function of
turbine layout. We train the model using a multi-fidelity Gaussian
Process (GP) regression with a combination of low (engineering wake
model) and high-fidelity (Large-Eddy Simulations) simulations of farms
with different layouts and wind directions. A large set of low-fidelity
data speeds up the learning process and the high-fidelity data ensures a
high accuracy. The trained multi-fidelity GP model is shown to give more
accurate predictions of CT* compared
to a standard (single-fidelity) GP regression applied only to a limited
set of high-fidelity data. We also use the multi-fidelity GP model of
CT* with the two-scale momentum theory
(Nishino & Dunstan 2020, J. Fluid Mech. 894, A2) to demonstrate that
the model can be used to give fast and accurate predictions of large
wind farm performance under various mesoscale atmospheric conditions.
This new approach could be beneficial for improving annual energy
production (AEP) calculations and farm optimisation in the future.