A Data-Driven Reduced-Order Modeling Method for Dynamic Wind Farm Control

Wake effects impose significant aerodynamic interactions among wind turbines. Advanced wind farm control considering the wake dynamics has become of great importance for the wind energy integration into the power grid. To address this issue, a control-oriented dynamic wind farm model is essential, which needs to be able to capture the dominant aerodynamic characteristics while ensuring a high computational efficiency. In this paper, a data-driven reduced order method is adopted to develop a low-order dynamic WF model. The control-oriented model captures the dominant flow dynamics seen by high-fidelity simulations from the data perspective. Besides, the original input-output relation is well preserved. Thus, the proposed low-order surrogate model is promising to be used in wind farm dynamic control.