Adaptive fuzzy-recurrent neural network tuned fractional-order distributed control for robust frequency regulation in multi-microgrid systems

This paper presents an advanced frequency control solution for multi-microgrid systems (MMGS) with high renewable energy penetration, where conventional control methods struggle with scalability and disturbance resilience. We propose a Distributed Consensus Control Strategy (DCS) using a Fractional Order PID (FOPID) controller adaptively tuned by a Fuzzy-Recurrent Neural Network (FRNN). Our method delivers significant improvements in both transient and steady-state performance compared to established approaches. Extensive real-time hardware-in-the-loop (HIL) testing on a three-microgrid platform demonstrates the superiority of the proposed controller. In MG1, it reduces settling time by 29% (4.19 s vs. 5.93 s for PID), lowers peak overshoot by over 90% (0.0007 vs. 0.0076 for PID), and cuts absolute error by more than 80% (1.017 vs. 6.32 for PID). Similar improvements are validated across MG2 and MG3, and comprehensive scenario testing confirms the method's robustness under diverse disturbances. The proposed FRNN-tuned FOPID-based DCS stands out as the first adaptive, distributed framework offering real-time self-optimization and exceptional resilience for frequency regulation in MMGS. Its demonstrated performance and adaptability make it a leading candidate for future smart grids and cyber-physical energy systems.