The Simple Power-Based Modulation Methods for DAB-Based AC-DC Converter with Unfolder Concept
Bibliographic record
Abstract
The DAB-based AC-DC converter with isolated ability has been studied to connect the ac grid and the dc bus with unfolder operation for high efficiency performance, and the DAB dc-dc stage is employed to realize the half sinusoidal modulation. However, in terms of efficiency, the adopted modulation methods in the existing methods may be not very suitable for the DAB converter, and a minimum-current-stress phase-shift (MCS-PS) method is selected which can implement on-line continuous control with high efficiency for this converter. Generally, there are two kinds of load conditions for this AC-DC converter, including the resistor load and the grid voltage source. When the DAB-based AC-DC converter is connected with the load resistor, the accurate power flowing model is analyzed, and then, a simple power-based modulation scheme (SPBM) is proposed for high power quality. Moreover, when this AC-DC converter is connected with the grid voltage source, a SPBM scheme is also proposed for high power quality and regulating both the active power and the reactive power. Finally, the simulation and experimental results are provided to verify the excellent performance of the proposed SPBM strategies.
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.
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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".