Metaheuristic Based MPPT Control for Solar PV Systems Under Partial Shading Conditions

Abstract

Partial shading significantly impacts a PV array’s ability to generate power. The array’s modules will produce various row currents.Thus the panels must be modified for row current difference minimization to maximize the power extraction from the PV array. Under uniform insolation, conventional Maximum Power Point Tracking (MPPT) Hill climbing approaches such as Perturb and Observe and Incremental Conductance(INC) can effectively track the maximum power point, but they fail under partial shaded conditions. Physical panel relocation may involve time-consuming operations and extensive interconnection ties. Meta-Heuristic algorithms for effective shadow dispersion is an appealing approach. In this paper, an MPPT control method based on meta-heuristic Grey Wolf Optimization (GWO)is implemented in a Triple Tied (TT) configuration to track the global maximum power point(GMPP) under 5 distinct partial shading scenarios.The performance indices such as mismatch power, fill factor and convergence time of GWO results are compared with INC, Particle Swarm Optimization(PSO) and Horse Herd Optimization(HHO) algorithms. The GWO approach is found to perform better than INC,PSO and HHO in tracking the GMPP with better accuracy and less computational time in all shading conditions.