Integrated PV energy generation system with high-gain converter and CHHO-FLC-based MPPT for grid integration

The increasing global reliance on Renewable Energy Resources (RES) presents significant challenges in efficiently harnessing and integrating these resources into existing energy infrastructures. As energy demands continue to rise, there is a critical need for advanced systems that optimizes the power generated from renewable sources, including photovoltaic (PV) systems, and ensures their stable incorporation into the power grid. Thereby, this paper proposes a system that integrates a PV power generation system with a high-gain boost converter, which plays a critical role in efficiently managing power flow among the PV source and grid. This ensures that the highest available power from the PV panel is effectively utilized, enhancing overall system efficiency. Notably, an innovative fuzzy logic controller (FLC)-chaotic horse herd optimization algorithm (CHHO) approach is deployed for maximum power point tracking (MPPT), enhancing the system's energy harvesting capabilities. The FLC technique adapts to dynamic environmental conditions by adjusting its variables, while the CHHO algorithm's optimization capabilities help in finding the optimal operating point even in challenging conditions. Furthermore, the output of converter is directed to a single-phase voltage source inverter (VSI) connected to the grid. The VSI is controlled by a conventional proportional-integral (PI) controller, ensuring stable and synchronized power injection into the grid. The proposed system addresses vital challenges in renewable energy integration, including efficient power management, MPPT optimization and grid integration, thereby promoting to the progression of clean energy technologies and their integration into the mainstream energy infrastructure. The developed system is implemented in the MATLAB/Simulink platform to confirm the efficacy of the proposed research, which demonstrated that the proposed high-gain boost converter has a maximum efficiency value of 97.1% when compared to other approaches.