A Photovoltaic Model with Reduced Computational Time
Bibliographic record
Abstract
Modeling partially shaded photovoltaic (PV) systems for online applications such as model-based MPPTs requires a PV circuit model with low computational time to simulate the large number of connected PV units within a reasonable amount of time. Unfortunately, the accurate PV models available in the literature are complex and suffer from high computational time due to their dependence on a transcendental implicit equation. This paper proposes a photovoltaic circuit model featuring lower computational time and comparable accuracy. The model utilizes the accuracy of the practical PV model and reduces the computational time by replacing the model series resistance with a third-degree-polynomial voltage-dependent source. The proposed model mimics the accurate characteristics of the practical model without being dependent on a transcendental implicit equation, thus providing low computational time. The model also introduces a new parameter to enhance the model's accuracy at low irradiance. The effectiveness of the model is shown by comparing the computational time and accuracy of the proposed model with those of the available models. A case study of partially shaded PV systems shows that the percentage of reduction in computational time improves with increases in the number of PV units in a simulated PV system, providing a clear advantage when simulating large PV systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".