A Minimum Power Loss Approach for Selecting the Turns Ratio of a Tapped Inductor and Mode of Operation of a 5-switch Bidirectional DC-DC Converter
Bibliographic record
Abstract
One of the most important aspects for the reliable operation of a DC Microgrid is the DC-bus voltage regulation within the system. For this, Supercapacitors (SCs) are widely used as auxiliary devices since they can provide sudden bursts of power and demand/supply currents with high gradients. Since commercial SCs usually present low voltage levels and, with the increasing trend of raising the DC-bus voltage, their connection in such systems requires power electronics interfaces capable of providing a wide range of voltage conversion. In this scenario, Tapped Inductors (TIs) provide a means for achieving low/high voltage gains in non-isolated DC-DC converters with neither too small nor too large duty cycles (D) by adjusting their turns ratio (n:1) to a desired value at a given operating point. However, a proper methodology for the actual choice of this value and the impacts of this on important aspects of the system, such as the converter efficiency, are still lacking in the literature. This way, this paper concerns a 5-switch DC-DC converter, based on a TI and that presents voltage gains that vary with D with Buck, Boost and Buck-Boost characteristics, where the main objective is to present an approach to determine the mode of operation and the turns ratio of the TI for reducing the losses on the power switches.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".