ABSTRACT The large‐scale integration of renewable energy sources such as photovoltaic and wind farms presents significant challenges to voltage stability and fault‐ride through capability, particularly in weak transmission networks characterized by low inertia and high impedance. Advancing carbon neutrality and energy sustainability demands flexible and adaptive control strategies capable of supporting diverse renewable technologies. This research introduces a cascaded control parameter optimization framework to enhance system stability and resilience in scenarios with high renewable energy penetration. Central to this framework is developing a novel dynamic resilience metric, which guided the optimization process by minimizing transient, extending permissible fault‐clearing times, and strengthening post‐recovery. An enhanced particle swarm optimization algorithm is employed to concurrently optimize parameters across plant models, electrical systems, and generator controllers, all in alignment with the PJM model development guidelines. This framework is validated on the Simplified 14 Generator Test System (Area 5), representative of Southeast Australia's grid, and verified for compliance with AEMC fault‐clearing requirements and IEEE 1947‐2003 standard. Case studies demonstrate its effectiveness and adaptability, with voltage overshoot reduced from 25%–40% to 6%–25%, and fault clearing times extended from 55–70 ms to 255–270 ms. These results confirm that the proposed approach offers a robust solution for integrating renewables into weak grids, enhancing reliability and supporting the shift toward a sustainable energy future.
Sakib et al. (2026) studied this question.