Transient Stability Enhancement Using Hybrid Power System Stabilizer in Renewable-Integrated Power Systems

This study introduces a hybrid Power System Stabilizer (Hybrid-PSS) that combines adaptive gain scheduling with an AI-based neuro-fuzzy tuning system to improve transient stability in renewable-integrated power systems. The high use of Doubly Fed Induction Generators (DFIG) and Permanent Magnet Synchronous Generators (PMSG) leads to lower inertia and changed electromechanical behavior, which limits how well conventional PSS work. A two-machine system was modeled in MATLAB with 35% renewable share to test the model under three-phase and line-to-ground (LG) fault conditions. Simulation results show that during a 1s three-phase fault, the rotor angle deviation with conventional PSS peaks at about 0.30 rad°, while the DFIG and PMSG show smaller swings of approximately 0.25 rad and 0.20 rad °, marking a slight reduction. Frequency deviation decreases from 1.03 Hz with conventional PSS to 0.89 Hz with Hybrid-PSS, showing a 14.58% improvement in frequency stability. After a fault, bus voltage recovery significantly increases from 0.72 p.u. with conventional PSS to 0.95 p.u., achieving a 33% performance gain. The damping ratio of the critical electromechanical mode rises from 0.18 with conventional PSS to 0.23 to 0.25, with Hybrid-PSS, reflecting about24. % improvement. These results confirm that the Hybrid-PSS offers better damping, faster stabilization, and greater grid resilience with high renewable penetration. The proposed approach provides a practical solution for improving grid stability in future renewable-focused power systems.