Compare the Convergence Behavior of DE and PSO Optimization Algorithms for Parameter Extraction in DDM Equivalent Circuit for the PV Panels
Bibliographic record
Abstract
Accurate equivalent circuit parameter estimation for solar cells can significantly provide actionable insights for photovoltaic (PV) system designers. In this paper, we present a comparative analysis of two commercial PV modules, Jinko JKM365M (monocrystalline) and Canadian Solar CS3U-365PB-FG (polycrystalline bifacial), tested under distinct real-world environmental conditions, which were conducted under high irradiance with elevated temperature (1000 W/m², 65°C) and low irradiance with moderate temperature (200 W/m², 25°C). A rigorous preprocessing pipeline was applied to enhance data quality and ensure the reliability of the extracted parameters. Both Differential Evolution (DE) and Particle Swarm Optimization (PSO) were implemented in MATLAB and evaluated based on convergence behavior, Root Mean Square Error (RMSE), and alignment with manufacturer specifications. The key findings emphasized the performance of the optimization algorithms and the accuracy of the models. This study makes several noteworthy contributions to the field of photovoltaic modeling, particularly in the context of parameter estimation using field-measured data. One of the primary achievements lies in validating the efficacy of DE as a superior optimization algorithm for PV applications. The findings contribute to more accurate PV modeling, improved system diagnostics, and enhanced design and control of solar energy systems.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".