A New Maximum Power Point Tracking With a Combined Particle Swarm Optimization–Biogeography-Based Optimization Algorithm for Photovoltaic System

The amount of power produced by a solar panel depends on the intensity of radiation and surrounding temperature. Optimizing the performance of photovoltaic systems requires the operation of solar panels at the maximum power point (MPP). In the present paper, a novel maximum power point tracking (MPPT) method is introduced based on an intelligent algorithm. The proposed method, called hybrid MPSO-MBBO, combines modified biogeography-based optimization (MBBO) and Modified Particle Swarm Optimization (MPSO) algorithms. The performance of the presented algorithm is compared with perturb and observe (P&O) and genetic algorithm (GA) as well as MPSO and MBBO. The effectiveness of the proposed method is further verified by experimental and simulation results in a typical photovoltaic system. The system under study includes a solar panel, an MPPT controller, and a DC–DC converter. To assess the accuracy of the proposed ‎ method, algorithms were implemented by the microcontroller STM32F407VGT6. The results showed that the MBBO algorithm had a higher speed response and the MPSO algorithm resulted in better accuracy, therefore, a combination of the two algorithms was used to track the MPP so that the MPSO algorithm is executed when the irradiance is uniform and the MBBO algorithm is executed when the irradiance has rapid changes.