Maximum power point tracking (MPPT) optimization using B&R controller embedded with best exploration region-Jaya algorithm for photovoltaic (PV) systems under uniform and partial shading conditions

This paper presents a novel optimization algorithm, Best Exploration Region and Jaya (BER-Jaya), for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems. The BER-Jaya algorithm enhances MPPT boost converter performance, especially under uniform and partial shading. A B&R controller is used for experimental validation. The Jaya optimization iteratively refines solutions based on improvement without mutation or crossover operations, moving solutions toward better ones and updating them incrementally considering the current best solution until a termination criterion is met. The BER-Jaya algorithm enhances the Jaya algorithm's evolution ability with a modified Gaussian function for better exploration. The Jaya drawbacks include premature convergence, lack of diversity, stagnation, and suboptimal convergence rates, highlighting the need for BER-Jaya enhancements. Two case studies evaluate BER-Jaya's efficacy against conventional techniques, including Arithmetic Optimization Algorithm (AOA), Grey Wolf Optimizer (GWO), and Jaya. Experiments under uniform and partial shading simulate real-world PV system scenarios. Results show BER-Jaya outperforms existing techniques in tracking maximum power point (MPP) under dynamic shading, with superior convergence speed and accuracy, enhancing MPPT boost converter efficiency and performance. This study advances optimization algorithms for MPPT applications, offering insights into B&R controller implementation and BER-Jaya algorithm performance in real-world PV systems under challenging conditions.