Dominant-Mode-Based SCR-Adaptive SG-PSO Tuning for LVRT Recovery of PMSG Wind Turbines in Weak Grids

Transient instability during the low-voltage ride-through (LVRT) recovery of permanent magnet synchronous generator (PMSG) wind turbines is strongly influenced by weak-grid interactions, while the quantitative relationship among grid strength, control parameters, and recovery performance remains insufficiently understood. This paper develops a small-signal transient recovery characteristic matrix for a grid-connected PMSG system by incorporating the dynamic interactions among the phase-locked loop (PLL), inner current loop, DC-link voltage loop, and grid-side inductance. Dominant-mode and root-locus analyses are employed to investigate how variations in the short-circuit ratio (SCR) affect dominant eigenvalue trajectories and the sensitivities of six PI control parameters. Based on the identified dynamic mechanisms, an SCR-adaptive sensitivity-guided particle swarm optimization (SG-PSO) method is proposed for coordinated PI parameter tuning. The proposed approach introduces SCR-dependent damping constraints and physical feasibility constraints, while normalized real-part eigenvalue sensitivities are utilized to guide the optimization process toward the most influential control parameters. Comparative simulation results demonstrate that, under SCR = 1.5, SG-PSO reduces the point of common coupling (PCC) voltage overshoot to 1.4% and shortens the recovery time to 58 ms, achieving better transient recovery indices than conventional PSO and OOBO-PI under the same simulation and constraint settings. Under SCR = 2.5, the recovery time is further reduced to 46 ms while maintaining a low overshoot of 0.9%. Additional robustness tests under parameter uncertainties and fault-condition variations further support the effectiveness and adaptability of the proposed method. The results indicate that the proposed SG-PSO framework provides an effective solution for enhancing LVRT recovery performance of PMSG wind turbines operating in weak-grid environments.