MPPT Efficiency in PV Arrays under Partial Shading Conditions:A Comparative Analysis of PSO and P&O Algorithms

Global Maximum Power Point Tracking (GMPPT) presents a fundamental challenge in photovoltaic (PV) systems due to the inherent nonlinearity of PV array characteristics. Partial shading (PS) emerges as a particularly critical factor, significantly compromising overall system efficiency by inducing multiple local maxima in the Power-Voltage (P-V) characteristic curve. While conventional tracking algorithms demonstrate adequate performance under uniform irradiation conditions, their effectiveness diminishes substantially under Partial Shading Conditions (PSCs), where they frequently converge to local power peaks rather than the true Global Maximum Power Point (GMPP). To address these limitations, intelligent computational approaches have been developed as robust alternatives for reliable GMPP tracking in complex shading environments. This investigation presents a comparative analysis of two established algorithms: Particle Swarm Optimization (PSO) and Perturb and Observe (P&O), evaluating their respective capabilities in GMPP identification. Extensive simulation studies conducted across diverse shading patterns conclusively establish the superior performance of the PSO algorithm, which consistently achieves steady-state tracking efficiencies exceeding 99% in all operational scenarios. These findings strongly suggest that PSO represents the more effective solution for optimal power extraction in PV systems operating under dynamic environmental conditions.