Impedance Modeling and Parameter Optimization Method for Wind Farms Considering Station-Level Control

• • Decoupling the MIMO system into positive/negative-sequence SISO models reduces stability analysis complexity while preserving frequency coupling effects, enabling the use of the traditional Nyquist criterion; this matters industrially because it provides a computationally tractable yet accurate method for assessing broadband oscillation risk in large wind farms, avoiding the over-simplification that leads to stability misjudgments in unbalanced or high-penetration scenarios. • • Station-level control parameters directly influence wind farm impedance characteristics; incorporating these parameters alongside collector line and individual turbine impedances improves impedance modeling accuracy, which is critical for reliable stability assessment and for designing control strategies that reshape output impedance to mitigate oscillations. • • Under specific weak-grid conditions, optimizing station control parameters via the ACS algorithm effectively reshapes the wind farm output impedance and suppresses broadband oscillations; this provides a practical pathway for enhancing grid-connected stability without requiring hardware modifications, offering a cost-effective solution for existing wind farms. • • The ACS algorithm outperforms GA, PSO, GWO, and SSA in optimizing wind farm control parameters, as validated on the RT-LAB platform; this superiority translates into more reliable convergence and better oscillation suppression, making ACS a preferred choice for field implementation where optimization efficiency and robustness are paramount.