Power Tracking and Performance Analysis of Hybrid Perturb-Observe, Particle Swarm Optimization, and Fuzzy Logic-Based Improved MPPT Control for Standalone PV System

The increasing energy demand and initiatives to lower carbon emissions have elevated the significance of renewable energy sources. Photovoltaic (PV) systems are pivotal in converting solar energy into electricity and have a significant role in sustainable energy production. Therefore, it is critical to implement maximum power point tracking (MPPT) controllers to optimize the efficiency of PV systems by extracting accessible maximum power. This research investigates the performance and comparison of various MPPT control algorithms for a standalone PV system. Several cases involving individual MPPT controllers, as well as hybrid combinations using two and three controllers, have been simulated in MATLAB/SIMULINK. The sensed parameters, i.e., output power, voltage, and current, specify that though individual controllers effectively track the maximum power point, hybrid controllers achieve superior performance by utilizing the combined strengths of each algorithm. The results indicate that individual MPPT controllers, such as perturb and observe (P&O), particle swarm optimization (PSO), and fuzzy logic (FL), achieved tracking efficiencies of 97.6%, 90.3%, and 90.1%, respectively. In contrast, hybrid dual controllers such as P&O-PSO, PSO-FL, and P&O-FL demonstrated improved performance, with tracking efficiencies of 96.8%, 96.4%, and 96.5%, respectively. This research also proposes a new hybrid triple-MPPT controller combining P&O-PSO-FL, which surpassed both individual and dual-hybrid controllers, achieving an impressive efficiency of 99.5%. Finally, a comparison of all seven cases of MPPT control algorithms is presented, highlighting the advantages and disadvantages of individual as well as hybrid approaches.