PSO and GA-based maximum power point tracking for partially shaded photovoltaic systems

Abstract

Under partial shading (PS) conditions, photovoltaic (PV) systems are popularly known to suffer from low-energy efficiency. Therefore, an effective MPPT algorithm should be used to detect the unique global peak as the maximum power point (MPP), and avoid any local maxima in order to mitigate the effect of PS. To date, various MPPT techniques have been developed to reliably track the MPP under all circumstances and reduce the energy losses due to PS. Usually, conventional methods such as Perturb and Observe (P&O) and the Incremental Conductance (IncCond), fail to extract the global MPP of the PV panel if the PV generator is partially shaded. To overcome this problem, Evolutionary Algorithms (AEs), namely the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are studied, simulated and compared under the same software.