Comparative Study of Population-Based Algorithms for Solar PV Array Reconfiguration System for Partial Shading Conditions

Photovoltaics are one of the widely used renewables and play an indispensable role in meeting the increased energy demand in recent times. Partial shading condition occurs due to non-uniform irradiance across the PV panels which greatly reduces the efficiency and output of the PV plants. Several techniques were developed as a tool to reduce the vulnerability of partial shading in literature out of which PV array reconfiguration system is found to be a competent technique for obtaining the maximum power in such conditions. The dynamic reconfiguration of PV system is accomplished by redistributing the modules based on their shading levels. Therefore, this paper compares different algorithms based on population such as Differential Evolution (DE), Rao’s optimization, Particle Swarm optimization (PSO), Reptile Search Algorithm (RSA) and Manta Ray Foraging optimization (MRFO) aiming to provide the optimum pattern for PV reconfiguration to produce maximum power output under varied conditions of partially shaded PV array. The presented work considers PV array in total-cross-tied (TCT) configuration and the results are compared considering the metrics like maximum power output obtained and fill factor.