Hybrid Control Strategies for Switching Between Grid-Connected and Islanded Operation in Renewable Microgrids

Abstract

The integration of renewable microgrids into modern power systems requires seamless transitions between grid-connected and islanded operations to ensure energy reliability and efficiency. However, conventional switching mechanisms struggle with voltage fluctuations, frequency instability, and synchronization issues arising from the intermittent nature of renewable energy sources. This paper presents a hybrid control strategy, combining AI-driven predictive modeling, adaptive droop control, and synchronized phasor measurements for optimized mode transitions. Reinforcement learning algorithms enhance decision-making by adjusting switching thresholds dynamically based on real-time grid conditions, reducing transition instability by 35% and improving synchronization accuracy to 92%. Additionally, STATCOM-based reactive power compensation mitigates voltage deviations, ensuring power quality. The proposed framework is validated through MATLAB/Simulink simulations, demonstrating superior stability and scalability compared to traditional control methods. These findings contribute to advancing smart grid modernization, enabling robust microgrid operations under variable renewable energy conditions, and supporting the global shift to decentralized energy systems.