Multiobjective adaptive predictive virtual synchronous generator control strategy for grid stability and renewable integration

Abstract

Abstract A novel Adaptive Predictive Virtual Synchronous Generator (AP-VSG) control strategy is proposed for enhanced grid stability and seamless renewable energy integration. The method introduces adaptive inertia and damping mechanisms combined with multi-objective predictive optimization, specifically designed for parallel-connected Self-Excited Induction Generators (SEIGs). Unlike conventional approaches requiring multiple DC conversion stages, the proposed system implements parallel operation directly in the AC domain, reducing system complexity and conversion losses. The AP-VSG control incorporates a real-time adaptation of virtual inertia (H) ranging from 1 to 4 s and damping coefficient (D) from 20 to 65 pu, responding to grid frequency deviations and Rate of Change of Frequency (RoCoF). Experimental validation with parallel-connected 2.2 kW and 5.5 kW SEIGs demonstrates a 56% reduction in maximum RoCoF (from ± 0.48 Hz/s to ± 0.21 Hz/s), 33% improvement in frequency nadir (50.85–50.87 Hz), and 41% enhancement in damping ratio. Under fault conditions, the system maintains a current limit of 1.5 pu while providing reactive support up to 0.8 pu. The multi-objective optimization framework achieves 36.7% reduction in control effort while maintaining stability margins (PM $$>45^{\circ }$$ , GM>6 dB). Statistical analysis confirms 95th percentile frequency regulation enhancement of 43.5% compared to conventional VSG control. The fault ride-through capability demonstrates voltage recovery within 100 ms with THD maintained below 3%. Experimental results verify robust performance under various grid disturbances, including voltage sags down to 0.2 pu and complete grid disconnection scenarios.

Keywords

• Adaptive control
• Multi-objective optimization
• Predictive control
• Renewable energy integration
• Virtual synchronous generator
• Smart grid technology