Impedance Modeling and Parameter Optimization Method for Wind Farms Considering Station-Level Control
Large-scale wind farms integrated into weak grids are susceptible to broadband oscillations, a problem that existing impedance models and control parameter optimization methods fail to address systematically because they neglect station-level control and frequency coupling effects. This paper proposes a station-level control strategy based on available power allocation and an adaptive compass search (ACS) algorithm for optimizing station control parameters. A sequence impedance method incorporating frequency coupling effects establishes the aggregated wind farm impedance, and the grid-connected multiple-input multiple-output (MIMO) system is decoupled into positive- and negative-sequence single-input single-output (SISO) impedance models. A high-precision wind farm impedance model that accounts for station-level control is constructed, and the Nyquist criterion evaluates the suppression effect of station control on broadband oscillations. The ACS algorithm optimizes station control parameters to enhance the adaptability of wind farm impedance to external grid impedance, thereby reducing oscillation risk. RT-LAB platform simulations validate the impedance modeling method and control parameter optimization. Results demonstrate that considering station-level control yields a high-precision wind farm impedance model. Compared with genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), and sparrow search algorithm (SSA), ACS is more suitable for optimizing wind farm control parameters. Under specific weak-grid conditions, ACS-optimized station control parameters effectively improve grid-connected stability of the wind farm.