Robust Method for Estimating Single-Diode Model Parameters of a PV Module from the Manufacturer's Datasheet Information
Bibliographic record
Abstract
Estimating the electrical model parameters of photovoltaic (PV) modules, which are normally not provided by manufacturers, is crucial for modeling PV systems in simulation studies. This work proposes a novel robust approach to identify parameters of the single diode model (SDM) of a PV module. The equivalent electrical circuit parameters are extracted by solving a set of equations through the application of the Lambert W function for a given value of ideality factor, Ai, based on data found in manufacturer's datasheets under standard test conditions (STC). To find the optimal ideality factor, parameters are computed for different Ai values within a defined range, and their fitness is assessed using a multi-criteria objective function. Irradiance and temperature dependence of the parameters are estimated using the temperature coefficients available on datasheets and the irradiation coefficient of open circuit voltages, calculated using the data at Normal Operating Cell Temperature (NOCT). By utilizing the estimated values for the parameters, the current-voltage (1- V) characteristics at different levels of irradiation and temperatures, and the temperature coefficient of maximum power for several PV modules, were obtained. The errors expressed in terms of root means square deviations compared to the datasheet values were less than 4 % for the five different PV modules considered.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".