Hybrid microgrids integrating distributed renewable energy with energy storage and hydrogen systems face fundamental stability challenges. Unlike conventional power plants, converter-based generators—such as solar inverters and wind turbines—lack natural inertia, making DC-bus voltage regulation difficult during rapid load changes or generation fluctuations.
Researchers have proposed a novel intelligent control framework combining backstepping sliding mode control with adaptive neural-fuzzy tuning to address this problem. The system emulates synthetic inertia using a virtual capacitor and actively manages power exchange across AC and DC portions of the microgrid while coordinating distributed generation units—photovoltaic arrays, wind turbines, batteries, and hydrogen electrolyzers and fuel cells.
The control strategy employs real-time adaptive tuning through artificial neural networks, allowing controller parameters to adjust automatically as microgrid conditions change. Stability is mathematically verified using control Lyapunov functions, providing formal assurance of safe operation across a range of operating conditions.
Detailed computer simulation modeled a complete microgrid with solar generation, permanent-magnet synchronous generator wind turbines, lithium-ion battery storage, electrolyzer, and proton exchange membrane fuel cells. The system incorporated neural-network-based maximum power point tracking to optimize renewable energy extraction. Performance was rigorously tested under severe disturbances and variable loads against two conventional control approaches.
Results demonstrate substantial improvements: the proposed controller reduced voltage overshoot by 75–100 percent, cut response rise time by 58–80 percent, and achieved zero steady-state error in converter-in-the-loop testing. Most significantly, converter efficiency reached 95.78 percent compared to 69.21 percent for reference designs, indicating dramatically improved energy utilization.
These findings have broad implications for grid operators deploying high-penetration renewable microgrids, particularly those incorporating hydrogen storage for long-duration energy management. The framework enables stable, efficient operation of complex hybrid systems without requiring additional hardware, relying instead on advanced control algorithms.
