A novel Adaptive Jellyfish Search algorithm (AJFS) for maximum power optimization of photovoltaic systems: performance evaluation and analysis

In solar photovoltaic (SPV) systems, optimizing output under various meteorological conditions relies on the Maximum Power Point (MPP) tracking controller. However, the presence of multiple peaks due to partial shading complicates this tracking process. While traditional and soft computing methods are commonly employed for MPP tracking, they face limitations such as the fixed step size in conventional methods and a lack of randomness in soft computing approaches once they reach a certain MPP. To address these challenges, a unique optimization technique known as the Adaptive Jellyfish Search (AJFS) method has been proposed. This method conducts both global and local searches simultaneously in a single step, enhancing the effectiveness of MPP tracking. To evaluate its performance and resilience, zero, weak, moderate, and strong shading patterns are employed in simulations, with comparisons made against traditional Jellyfish Search (JFS) and Particle Swarm Optimization (PSO) techniques. The newly suggested AJFS approach demonstrates significant improvements over traditional methods. It reduces convergence time by 49% and offers additional motivating features, including zero risk of failure, minimized oscillation in power parameters, and enhanced energy production by 1.5%. Moreover, it enables smooth tracking of the MPP, particularly in dynamically changing shading patterns. Overall, the AJFS method presents a promising solution for efficient MPP tracking in SPV systems, overcoming limitations of conventional approaches and demonstrating superior performance under various shading conditions. Its ability to simultaneously conduct global and local searches in a single step makes it well suited for optimizing energy production in real-world scenarios.