A Partial-Power-Processed CLLC-DAB DC/DC Transformer with Voltage Self-Balancing Capability for Bipolar LVDC Distribution Systems
Bibliographic record
Abstract
The bipolar low-voltage DC (LVDC) distribution system is an excellent approach to facilitate the flexibility and reliability of DC microgrids. This paper proposes a DC-DC transformer with bipolar multi-voltage output and self-balancing property. First, this configuration combines the efficient CLLC with the flexible DAB, in which the main power transferring via the CLLC and the DAB carrying partial power. Moreover, to achieve self-balancing, a pair of resonant LC branches are implemented on the secondary side of the CLLC and DAB converters, forming a new series resonant dual-active-half-bridge (SR-DHB) converter that enables partial self-balancing. Additionally, following the principle of volt-second balancing, inductors and capacitors can be added to the first/second arm of secondary side, completing the self-balancing property. Furthermore, precise regulation of output voltage can be achieved by merely adjusting the phase shift angle of the DAB. To improve the system’s dynamic response, a model-based voltage control method is proposed. Lastly, simulations reveal outputs of ± 750 V and ± 375 V, and experiments are conducted to prove the proposed scheme.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".