This paper proposes a new population-based hybrid particle swarm optimized-gravitational search algorithm (PSO-GSA) for tuning the parameters of the proportional-integral-derivative (PID) controller of a two-area interconnected dynamic power system with the presence of nonlinearities such as generator rate constraints (GRC) and governor dead-band (GDB). The tuning of controller parameters such as K_p, K_i, and K_d are obtained by minimizing the objective function formulated using the steady-state performance indices like Integral absolute error (IAE) of tie-line power and frequency deviation of interconnected system. To test the robustness of the propounded controller, the system is studied with system uncertainties, such as change in load demand, synchronizing power coefficient and inertia constant. The two-area interconnected power system (TAIPS) is modeled and simulated using Matlab/Simulink. The results exhibit that the steady-state and transient performance indices such as IAE, settling time, and control effort are impressively enhanced by an amount of 87.65%, 15.39%, and 91.17% in area-1 and 86.46%, 41.35%, and 91.04% in area-2, respectively, by the proposed method compared to other techniques presented. The minimum control effort of PSO-GSA-tuned PID controller depicts the robust performance of the controller compared to other non-meta-heuristic and meta-heuristic methods presented. The proffered method is also validated using the hardware-in-the-loop (HIL) real-time digital simulation to study the effectiveness of the controller.

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.

The increasing penetration of renewable energy sources (RES) and plug-in hybrid electric vehicles (PHEVs) has introduced significant frequency instability in interconnected microgrid (MG) systems, necessitating adaptive and robust control strategies for reliable operation. This paper proposes a fuzzy-explainable neural network (FxNN)-based distributed fractional-order PID (FOPID) controller for active frequency regulation in interconnected microgrids. The control problem is formulated within a Lyapunov-based optimization framework, where system stability is ensured through a recursively updated energy function and differential learning dynamics of the explainable neural network. The proposed cascaded FxNN-FOPID controller is evaluated under varying load disturbances and intermittent renewable power. Simulation results demonstrate superior dynamic performance compared with conventional single-loop FxNN and PSO-GSA-based controllers. The proposed controller achieves a minimum settling time of 3.0 s, representing improvements of 14.3% and 30.2%, respectively. Furthermore, the integral absolute error (IAE) is reduced to 0.6292 × 10 ⁻³ , 0.5929 × 10 ⁻³ , and 0.6000 × 10 ⁻³ for MG1, MG2, and MG3, respectively, while the integral time absolute error (ITAE) is reduced by up to 93.6%. The controller also minimizes frequency oscillations with a peak magnitude of 0.0003 p.u. and achieves improved Integral of Squared Error (ISE) 0.1040 × 10 ⁻⁵ and Integral of Time-weighted Squared Error (ITSE) 0.9918 × 10 ⁻³ values. Owing to its adaptive gain tuning capability, the proposed controller maintains robust performance without requiring manual retuning under varying operating conditions. Comparative analysis confirms that the proposed cascaded FxNN-based distributed FOPID controller provides faster dynamic response, improved disturbance rejection, and superior low-frequency oscillation damping, making it an effective solution for reliable frequency regulation in renewable-energy-integrated microgrid systems. Furthermore, the proposed framework supports Sustainable Development Goals (SDG 7: Affordable and Clean Energy, SDG 9: Industry, Innovation and Infrastructure, and SDG 13: Climate Action) by facilitating resilient microgrid operation, enhancing renewable energy integration, and promoting low-carbon power systems.