Performance validation of global MPPT for efficient power extraction through PV system under complex partial shading effects
Bibliographic record
Abstract
The photovoltaic (PV) energy is essential for the future of sustainable energy developments. Conventional algorithms perform well in maximum power extraction under uniform irradiance conditions (UIC). However, they often struggle to maintain the global maximum power point (GMPP) under simple partial shading conditions (SPSCs), frequently getting stuck at local maximum power points (LMPPs) and resulting in power loss. This study developed an adapted perturb and observe based model predictive control (APO-MPC) maximum power point tracking (MPPT) approach in MATLAB/Simulink, comprising six series-connected PV modules, a boost converter, and load. The control strategy identifies GMPP and computes reference current to minimize the cost function of an optimization problem. It was compared with other MPPT algorithms regarding tracking accuracy, convergence speed, computational time, steady-state oscillations (SSOs), power efficiency under UIC, SPSCs, and complex partial shading conditions (CPSCs). The system was validated using real-time hardware implementation and seasonal field atmospheric data. The results indicated that the APO-MPC algorithm outperformed the others with no oscillations during GMPP tracking, average convergence time, and tracking efficiency of 0.17 s and 99.46%, respectively. The findings confirm its highly fast, accurate, and stable tracking of GMPP without getting trapped into LMPPs under CPSCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".