  As wind power penetration increases, the wind farms are required by newly
released grid codes to provide frequency regulation service. The most critical
challenge is how to formulate the dynamic model of wind farm for dynamic
control, since it is essentially is nonlinear and there are huge amount of
parameters to be maintained frequently. This paper proposes a data-driven model
predictive control (data-driven MPC) method to make wind farms participate
primary frequency regulation. In this method,a specialized dynamic mode
decomposition (SDMD) algorithm is developed, which can linearly approximate the
dynamics of wind farm from measurements based on Koopman operator
theory.Compared with the existing extended dynamic mode decomposition (EDMD)
method,this tailored SDMD has two advantages: 1) fully capturing the nonlinear
transients of wind turbine dynamics with good accuracy under a wide range of
working conditions; 2) much less computational burden with model dimensionality
reduction. Based on the recursively updated linear dynamic model, a model
predictive control solution is implemented. The simulation results show this
model-free solution can dynamically optimize wind turbine generators' active
power to track the frequency response requirement from system operator and
minimize the rotor speed distortion.
