A Photovoltaic Model with Reduced Computational Time
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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