Improved PSO-INC Algorithm for Maximum Power Point Tracking of Landscape-Oriented Photovoltaic Modules Under Dual-Peak Partial-Shading Conditions

Key numbers

• 3.48% between the simulated and
• 99.20% and a mean relative
• 0.80% across the six static-shading

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.

Huipeng Li, Xingrong Zhu, Jianyong Zhan, Linfei Feng, Linzhao Hao, Jicheng Zhou

Leverage2 / 5

Reusable within one subfield (a technique, dataset, or protocol a few groups will adopt).

16% of this desk’s rubric

Magnitude3 / 5

Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.

20% of this desk’s rubric

Evidence3 / 5

Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.

20% of this desk’s rubric

Novelty2 / 5

A new combination of known ideas.

10% of this desk’s rubric

Trajectory2 / 5

Some room to improve with obvious engineering.

14% of this desk’s rubric

Stakes3 / 5

Meaningful benefit to many people within a few years.

20% of this desk’s rubric

• Citations0 (reference 15, via openalex, Oct 24, 2026, 06:45 UTC)

• Reviewed by heuristic-v7 on Oct 24, 2026, 06:45 UTC. Paper type: empirical.
• Categories: Photovoltaic System Optimization Techniques, Metaheuristic Optimization Algorithms Research, Advanced DC-DC Converters, Renewable Energy, Sustainability and the Environment, Energy
• BRIEF, No.5 in the Climate & Energy edition of October 24, 2026.