Comprehensive Analysis of MPPT Control Approaches under Partial Shading Condition

Abstract

A photovoltaic array's output characteristic is non-linear, and its behavior varies with solar radiation and module temperature fluctuations. Therefore, a maximum power point tracking strategy is needed to improve the energy delivered by the photovoltaic systems. Due to multiple peak occurrences on the P-V curve, conventional techniques are restricted to the mismatch case. Consequently, this inquiry focused on a comparative assessment of four prevalent meta-heuristic methods for the stochastic search paradigm, including particle swarm optimization (PSO), gray wolf optimizer (GWO), artificial bee colony optimization (ABC), and cuckoo optimization (Cuckoo). MATLAB is utilized to develop the photovoltaic model, the Buck-boost converter, and the GMPPT algorithm implementation. The simulation outcomes demonstrate that the advanced GMPPT approach can handle the maximum power holding under a quick external environmental change and effectively manage the partial shading occurrences. Finally, this analysis suggests the potential use of PSO and GWO algorithms owing to the faster joining of the PV array's maximum power point compared to the Cuckoo and ABC strategies, considering well-dimensioned initial duty cycle values.