Bibliographic record
Abstract
The recently proposed modular multilevel dc-dc converter (M2DC) and the HVDC-dc auto transformer (HVDC-AT) enable direct power transfer between dc networks at low-to-moderate step ratios using only a single power electronic conversion stage. In both converter structures, an internal circulating sinusoidal ac current is established to balance the energy within the converter. In practice, however, the frequency of the circulating ac current is limited to a few hundred Hz, requiring significant ripple power to be filtered by large voltage submodule (VSM) capacitors. In this article, a novel energy transfer mechanism for interfacing dc networks is introduced wherein the frequency of the ac currents can be increased by one to two orders of magnitude, thus enabling a commensurate reduction in VSM capacitor size and cost. This is achieved by eliminating the ac chokes within the modular converter structure. Instead, a current source submodule (CSM) is employed to shape the circulating current, leading to a current of nearly square-wave shape with a frequency in the kHz range equal to the switching frequency of the CSM. This article describes one particular topology based on this current shaping mechanism, namely a high step down ratio dc-dc converter. The viability of the mechanism is demonstrated via simulation and experimental results from a laboratory scale implementation.
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) | 0.003 | 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".