Unified model of high step-up DC-DC converter with multi-cell diode-capacitor/inductor network
Bibliographic record
Abstract
Multi-cell diode-capacitor/inductor based boost derived DC-DC converter provides a simple solution for high step-up voltage regulation in solar and fuel cell generation. However, many passive components increase the order of system model and complexity. Transient modeling analysis reveals that time constant of each component in diode-capacitor/inductor network is much smaller than that of other circuit if the directly charging and discharge processes between capacitor through diode is fast enough. The voltage/current relationship of each capacitor/inductor in multi-cell network is fixed. Multi-cell diode-capacitor/inductor network can be seemed as multi-stage DC transformer. Based on the unique feature, this paper proposes the reduced-order modeling approach for high step-up DC-DC converter with multi-cell diode-capacitor/inductor network. Finally, simulation and experiments verify the correctness and effectiveness of new modeling approach. The unified reduced-order model contributes to better understanding of circuit characteristic and simplification of controller parameters design.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".