A New Fully Magnetically Coupled SiC-Based DC/DC Step-up LLC Resonant Converter with Inherent Balanced Voltage Sharing for Renewable Energy Systems with a Medium Voltage DC Grid
Bibliographic record
Abstract
A new fully magnetically coupled based LLC resonant converter is proposed in this paper that achieves balanced output voltage sharing for medium voltage (MV), high power applications. The modular approach of input-parallel output-series (IPOS) configuration with voltage multiplier is introduced to achieve high voltage gain and high power level. By magnetically-coupled the inductors and high-frequency transformers, the proposed converter can inherently achieve balanced output voltage sharing and significantly reduce the size and volume of the entire system. The modular LLC resonant circuit networks in the proposed converter allow zero voltage switching (ZVS) for all the primary-side SiC MOSFETs and ZCS turn-on and off in all the secondary-side SiC Schottky diodes over a wide load range. The output voltage sharing performance of the proposed converter is validated by simulation results of a 1.2kV/28kV, 130kW modular step-up converter, considering the variation of the parasitic parameters of the high-frequency magnetics and the resonant capacitances. Experimental results on a laboratory-scale SiC-based 6kW, 500V/6kV also verifies the performance of the proposed converter with 99% full-load efficiency.
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 | 0.001 |
| 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".