Precise three-diode photovoltaic model for photovoltaic modules based on Puma optimizer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The modeling accuracy of photovoltaic (PV) modules is essential nowadays due to the spread of PV power plant installation. Thus, precise PV modeling is vital as it ensures the optimal design, reliable performance prediction, and efficient energy management in solar power systems. The three-diode PV modeling is thus a suitable solution due to its precision and accuracy. However, it is complicated and includes nine unidentified parameters. The Puma optimization algorithm is presented in this paper for utilization in extraction and the optimization of nine unknown PV module parameters. The suggested methodology is applied to two commercial PV modules: the Kyocera KC200GT multi-crystalline and the Canadian PV monocrystalline modules CS6K280M. To ensure the superiority of the Puma algorithm, its results are compared with others resulting from more than four optimization algorithms, which include Artificial Electric Field Algorithm, Northern Goshawk Optimization, Grey Wolf Optimization, Coati Optimization Algorithm, and Particle Swarm Optimization algorithms. The Puma’s parameter extraction precision is further approved by the close agreement between the measured and estimated characteristics curves, extending its validation to variable temperature and irradiance level variations scenarios. The study establishes the Puma algorithm as a robust tool for parameter determination in the three-diode PV model. It opens new avenues for application in other complex optimization problems in renewable energy systems.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it