Grid Synchronization Algorithms for Intermittent Renewable Energy Sources Using AI Control Loops

The increasing integration of intermittent renewable energy sources (RES), such as solar photovoltaics and wind turbines, has introduced significant challenges to grid synchronization and stability. Traditional synchronization mechanisms, including Phase-Locked Loops (PLLs) and Voltage-Controlled Oscillators (VCOs), often struggle under weak grid conditions and high-frequency disturbances associated with inverter-based RES. This paper proposes a novel grid synchronization framework utilizing Artificial Intelligence (AI)-enabled control loops to adaptively track phase and frequency variations in real time. By incorporating neural network-assisted synchronization and reinforcement learning (RL)-based feedback tuning, the proposed architecture enhances phase detection accuracy, reduces synchronization delay, and improves power quality during grid fluctuations. A simulation-based evaluation is conducted on a renewable-integrated IEEE test system, demonstrating the efficacy of the AI control loops compared to conventional synchronization algorithms. The results show significant improvements in dynamic response time, synchronization stability, and harmonic suppression. The proposed solution offers a scalable and intelligent approach to synchronization in evolving smart grid environments.