Distributed Switching and Coupled Passives for High Performance Power Electronics
Bibliographic record
Abstract
Combining (1) many distributed switches and (2) coupled passives decouples the switching and passive access frequencies, fundamentally improving power electronics performance. The equality of the switching and passive access frequencies in single-switch power converter cells (e.g. buck) limits the achievable bandwidth and efficiency. Using distributed switches (e.g. multiphase, multilevel) and coupled passives (e.g., coupled inductors, series stacked capacitors, and transformers) together allows the passives to see a higher frequency than the switches; this enables the use of low, efficient switching frequencies and small, fast, high-frequency passives. This paper overviews the principles of a family of scalable power architecture with distributed switching and coupled passives, including frequency multiplication, passive balancing, transient inductance reduction, and internal dynamic consolidation. This theory is applied to design a 64× interleaved coupled inductor Li-Fi transmitter achieving above-switching-frequency communication-over-power with PAPR=1.2 dB and SFDR=35 dB while driving 400 W LEDs with 94.0% 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".