Multi-Criteria Evaluation of MPPT Algorithms under Time-Varying Partial Shading Using a Physics-Based PV Model
Bibliographic record
Abstract
This work investigates how widely used MPPT algorithms perform under realistic, timevarying partial shading and aims to provide an objective, reproducible basis for multicriteria decision-making.We implement a 60cell module model (three substrings with bypass diodes) driven by a 10s mixed shading profile at 1ms resolution; an internal oracle computes the instantaneous global MPP solely for scoring.Performance is quantified by oracle-normalized energy, a formally defined time-to-sustain 𝑡 95 , GMPP hit/dwell ratio, and a ripple/PRI; distributional behavior (histograms/CDF/boxplots), nonparametric inference (Kruskal-Wallis, Cliff's 𝛿), bootstrap confidence intervals, and a weight-transparent composite score (0.30 energy, 0.25 hit, 0.25 speed, 0.20 ripple) complement the analysis, with sensitivity/ablation on step sizes, swarm settings, attractiveness/Lé vy steps, and weights.Incremental Conductance achieves the highest energy (0.281 Wh), fast convergence (𝑡 95 = 3.0 ms), the best hit ratio (62.8%), and the lowest ripple (41.4 W).PSO yields intermediate energy (0.231 Wh) but long convergence (206.2 ms), FP responds fastest (𝑡 95 = 2.6 ms) yet records the lowest energy (0.094 Wh) and hit ratio (10.8%), and P&O is simple but ripple-prone (100.1 W) with lower energy (0.143 Wh).Despite universal large transient errors during regime changes (~220 W), distributional evidence and robustness checks consistently favor Incremental Conductance.The framework and metric suite offer reusable guidance for algorithm selection under partial shading and clarify when exploration latency or instability erodes net yield.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".