A unique novel-based FLC approach for enhancing MPPT operation of solar systems considering sudden/gradual variation...

Solar power is one renewable energy source that has great promise. Photovoltaic systems are becoming increasingly popular. Using maximum power point tracker technologies is essential for maximizing the amount of power that can be harvested from a solar system. The variability of the highest power point of a solar system is thought to be caused by the interaction of external elements with the system. In light of the foregoing, the study's overarching goal is to figure out how to use an innovative fuzzy logic controller to track a boost converter-based photovoltaic system's peak power point. We use the fuzzy logic controller to make the system more dynamically sensitive to changes in ambient temperature, quick and moderate variations in irradiance, and other environmental factors. The major objective of this research is to improve the fuzzy logic controller's scaling factors and membership functions. There is a correlation between these features and the controller's accuracy, stability, and speed. For the concept of optimization to be executed well, the cutting-edge metaheuristic method known as Arctic puffin optimization was employed. Arctic Puffin Optimization draws its motivation from nature. Comparisons and analyses with other effective optimization algorithms, such as particle swarm optimization and gray wolf optimizer, have shown that Arctic Puffin Optimization outperforms these other optimization procedures when it comes to fuzzy logic controller tuning. Using the MATLAB/Simulink R2020a environment, we test each method for tracking accuracy, efficiency, response time, transient overshoot, and steady-state ripple. A broad variety of weather conditions was used to conduct the investigation. The Arctic puffin optimization-based fuzzy maximum power point tracker controller's tracking efficacy was consistently over 99.8% in all the conditions investigated, according to the simulation findings.