Model predictive control of wakes for wind farm power tracking
CoRR(2024)
摘要
In this paper, a model predictive control scheme for wind farms is presented.
Our approach considers wake dynamics including their influence on local wind
conditions and allows to track a given power reference. In detail, a Gaussian
wake model is used in combination with observation points that carry wind
condition information. This allows to estimate the rotor effective wind speeds
at downstream turbines based on which we deduce their power output. Through
different approximation methods, the associated finite horizon nonlinear
optimization problem is reformulated in a mixed-integer
quadratically-constrained quadratic program fashion. By solving the
reformulated problem online, optimal yaw angles and axial induction factors are
found. Closed-loop simulations indicate good power tracking capabilities over a
wide range of power setpoints while distributing wind turbine infeed evenly
among all units. Additionally, the simulation results underline real time
capabilities of our approach.
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