Comparison and combination of axial induction and wake redirection control for wind farm power output maximisation and grid power reference tracking

This paper investigates the integration of axial induction control (AIC) and wake redirection control (WRC) in wind farms to optimise power output by addressing wake interactions among turbines that diminish wind speeds for downstream turbines. The study uses two analytical models, the Jensen Park and the Bastankhah Gaussian wake models, and three different software environments: FLORIS for wake effect simulation, FAST.Farm for mechanical load analysis, and WFSim for closed-loop simulations. The findings reveal that AIC, according to the Park model, offers potential power output improvements between 4% and 19% for wind turbine arrays of two to ten turbines, but WRC shows enhancements from 8% to 30% for the same number of turbines in an array, as calculated with the Gaussian model and the FLORIS software. However, a proof of concept in FAST.Farm with a two-turbine configuration demonstrates potential disadvantages of WRC, which increases tower loads considerably, motivating combined AIC-WRC to operate the turbine at the smallest yaw misalignment angle possible. Closed-loop simulations demonstrate a reduction in power reference tracking error by up to a factor of 3.9 using Koopman Model Predictive Control with combined AIC-WRC as compared to AIC control on the same simple two-turbine configuration.

Subjects

Axial Induction Control

Koopman

Model Predictive Control

Wake Redirection Control

DDC Class

600: Technology

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Comparison and combination of axial induction and wake redirection control for wind farm power output maximisation and grid power reference tracking