A Hybrid hGWO–ACO‐Tuned Interval Type‐II Fuzzy Multi‐Stage PID Controller for Robust Frequency Regulation in Renewable‐Dominated Isolated Microgrids

ABSTRACT The low inertia, nonlinear dynamics, and high penetration of renewable energy sources (RES) in islanded microgrids (MGs), combined with continuous load variations and inherent parameter uncertainties, pose significant challenges to maintaining frequency stability. Conventional proportional–integral–derivative (PID) controllers and single‐stage intelligent control schemes exhibit limited robustness against renewable intermittency, rapid operating condition changes, and modelling inaccuracies, thereby necessitating advanced adaptive load‐frequency control (LFC) strategies. This paper proposes a novel hybrid grey wolf–ant colony optimization‐tuned interval type‐II fuzzy logic multi‐stage PID (hGWO–ACO–T2F–MPID) controller for robust frequency regulation in isolated MG integrating photovoltaic (PV) systems, wind turbine generators (WTG), diesel energy generators (DEG), and plug‐in hybrid electric vehicles (PHEV). The proposed framework synergistically combines a MPID architecture to enhance transient response and steady‐state accuracy, an interval T2F inference mechanism to effectively manage nonlinearities, measurement noise, and parametric uncertainties through footprint‐of‐uncertainty modelling, and a hybrid hGWO–ACO metaheuristic algorithm that integrates the global exploration capability of grey wolf optimization (GWO) with the strong local exploitation ability of ACO to ensure rapid convergence and avoidance of local optima during controller tuning. Comprehensive MATLAB/Simulink simulations are conducted under diverse and realistic operating scenarios, including step load disturbances, solar and wind power fluctuations, simultaneous multi‐source disturbances, and severe parametric variations representing component aging and system uncertainties. Comparative analysis against conventional PID, fractional‐order PID (FOPID), MPID, and type‐1 fuzzy controllers optimized using standalone metaheuristic techniques demonstrates the superior dynamic performance and robustness of the proposed approach. Under step load disturbance, the integral time‐weighted absolute error (ITAE) is reduced from 3.758 × 10 − 4 to 1.197 × 10 − 4 , corresponding to a 68.2% improvement over the hGWO–ACO–MPID controller; during parameter uncertainty tests, the maximum frequency deviation decreases from 0.21–0.07 Hz (66.7% reduction), while the settling time shortens from 6.3–3.8 s (39.7% improvement). Furthermore, under combined wind, solar, and load disturbances, the proposed controller achieves the lowest oscillation amplitude, fastest recovery, and highest damping performance among all benchmarked methods. These findings confirm that the integration of interval type‐II fuzzy inference, MPID control, and hybrid hGWO–ACO optimization establishes a computationally efficient, highly robust, and adaptive frequency regulation framework suitable for renewable‐dominated isolated MG operating under severe uncertainty and dynamic disturbances.