Maximum power point tracking (MPPT) based on fuzzy logic and jellyfish optimization for solar photovoltaic systems

This study proposes an innovative approach to enhance the efficiency of photovoltaic (PV) solar systems by integrating a fuzzy logic-based Maximum Power Point Tracking (MPPT) algorithm, an optimized Proportional-Integral (PI) voltage controller, and the Jellyfish Optimization Algorithm (JOA). The primary objective is to maximize energy harvesting under varying irradiance and temperature conditions by achieving optimal power extraction. To overcome the limitations of conventional MPPT techniques-such as slow convergence, inaccurate tracking under partial shading, and poor performance in dynamic scenarios—the fuzzy MPPT provides intelligent and adaptive power tracking, while the JOA-based PI controller ensures optimal voltage regulation by minimizing oscillations and overshoot. The proposed system was modeled and tested using MATLAB Simulink 2022a, simulating a range of irradiance scenarios. The results demonstrate that the proposed approach significantly outperforms traditional MPPT methods by achieving higher power output, faster convergence, improved voltage stability, and overall system efficiency. The system attains a maximum power extraction efficiency of 98–99.5%, enabling effective battery charging and load power distribution. Nevertheless, certain limitations remain, including the computational complexity of JOA, the lack of hardware-based validation, and potential scalability challenges for large-scale PV installations. Future research should focus on hardware implementation, hybrid optimization techniques, and integration with smart grids to enhance adaptability and practical applicability. Overall, the findings contribute to the development of more intelligent, scalable, and sustainable renewable energy systems.