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Data-driven modelling of turbine wake interactions and flow resistance in large wind farms
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  • Andrew Kirby,
  • François-Xavier Briol,
  • Thomas Dunstan,
  • Takafumi Nishino
Andrew Kirby
University of Oxford

Corresponding Author:[email protected]

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François-Xavier Briol
University College London
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Thomas Dunstan
Met Office
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Takafumi Nishino
University of Oxford
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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.
02 May 20231st Revision Received
04 May 2023Submission Checks Completed
04 May 2023Assigned to Editor
04 May 2023Review(s) Completed, Editorial Evaluation Pending
05 May 2023Reviewer(s) Assigned
16 Jun 2023Editorial Decision: Accept