Comparative study of MPPT algorithm on photovoltaic string under partial shading

Abstract

Partial shading significantly degrades the photovoltaic (PV) performance value by introducing multiple peaks in power voltage (P-V) curve, complicating maximum power point tracking (MPPT). This research aims to presents a systematic comparative study of 4 MPPT algorithms, that are i) perturb and observe (P&O), ii) incremental conductance (InC), iii) particle swarm optimization (PSO), and iv) flower pollination algorithm (FPA), under 6 systematically testbeds modeled irradiance scenarios. The evaluation focused on tracking accuracy, convergence speed, and also robustness against local maxima entrapment. The findings indicated that slope-based algorithms (P&O and InC algorithm) achieved rapid convergence (<0.05 second), under uniform conditions but consistently fail to locate the global maximum power point (GMPP) in multi peak profiles, with efficiency dropping to ~83.6%. While the PSO algorithm succeeds in moderately faced complex cases, it fails under highly multimodal landscapes due to premature convergence. In contrast, for FPA algorithm consistently identifies the GMPP across all scenarios, its achieving superior tracking efficiency of up to 99.8%, despite requiring longer settling times (0.75-1.03 seconds) and exhibiting transient oscillations. These findings quantify the speed-accuracy trade-off in MPPT controllers and highlight the necessity of global optimization for reliable performance under dynamic shading. Future work will address the challenge of hardware implementation in the loop validation and computational efficiency for real-time embedded applications.