Data from: Hierarchical power control of a large-scale wind farm by using a data-driven optimization method

With the participation in automatic generation control (AGC), a
large-scale wind farm should distribute the real-time AGC signal to
numerous wind turbines (WTs). This easily leads to an expensive
computation for a high-quality dispatch scheme, especially considering the
wake effect among WTs. To address this problem, a hierarchical power
control (HPC) is constructed based on the geographical layout and
electrical connection of all the WTs. Firstly, the real-time AGC signal of
the whole wind farm is distributed to multiple decoupled groups in
proportion of their regulation capacities. Secondly, the AGC signal of
each group is distributed to multiple WTs via the data-driven
surrogate-assisted optimization, which can dramatically reduce the
computation time with a small number of time-consuming objective
evaluations. Besides, a high-quality dispatch scheme can be acquired by
the efficient local search based on the dynamic surrogate. The
effectiveness of the proposed technique is thoroughly verified with
different AGC signals under different wind speeds and directions.