Design Procedure for Partial-Power Processing Configurations Considering Gain, Power Rating, and Operating Region of the Converter
Bibliographic record
Abstract
A full-power processing (FPP) converter can be reconfigured in specific input-output connections so that it only processes a fraction of the output power. These configurations, known as Partial-Power Processing Configurations (PPPCs), typically force the converter to operate at a different operating point compared to FPP under the same input-output voltage and current conditions. Since converters are usually designed for FPP operation, there is a need to translate the PPPC input-output conditions and specifications into the conventional FPP converter design procedure. This paper introduces a design approach for PPPCs, focusing on the optimal configuration selection through a detailed mapping of PPPC specifications and operating regions into converter specifications and operating regions used within PPPCs. A generalized analysis of PPPCs is presented in per-unit terms, examining power rating, gain, and operating regions. Step-up PPPCs are compared while considering real, non-ideal converter parameters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".