Under partial-shading conditions, landscape-oriented photovoltaic (PV) modules can exhibit dual- or multi-peak power–voltage (P–V) characteristics, which may trap conventional maximum power point tracking (MPPT) algorithms at local optima. A 60-cell full-cell PV module model incorporating bypass diodes was established, and global maximum power point tracking (GMPPT) simulations were conducted under partial-shading conditions. The model agreed well with previously obtained experimental data, with a mean absolute percentage error of 3.48% between the simulated and measured powers. An improved particle swarm optimization–incremental conductance strategy (PSO-INC) was developed by incorporating a transition stage, an irradiance-change-triggered global-search restart mechanism, and a search-direction-dependent adaptive step size. This strategy combines the global exploration capability of particle swarm optimization (PSO) with the local refinement capability of incremental conductance (INC). Thirty independent simulations with prescribed random seeds were performed for each scenario. Relative to the experimentally measured global maximum power point (GMPP), PSO-INC achieved a mean tracking efficiency of 99.20% and a mean relative error of 0.80% across the six static-shading configurations. Its mean settling time was 44.81% shorter than that of PSO. Under 25% shading, the mean tracked power was 256.23 W, exceeding the reported outputs of INC and jump-exploration incremental conductance (JEINC) by 41.95 W (19.58%) and 40.10 W (18.55%), respectively. Following irradiance transitions from 1000 to 500 W/m2 and from 1000 to 0 W/m2, the mean settling time was reduced by 53.03% and 53.05%, respectively, relative to PSO. These results demonstrate that PSO-INC improves GMPPT accuracy, settling performance, and steady-state behavior under partial-shading conditions and rapidly changing irradiance.
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Li et al. (2026) studied this question.
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