Bibliographic record
Abstract
Abstract Power electronic converters serve as the standard interface between source and load in almost any electrical equipment. They have power semiconductor devices that are operated as ON/OFF switches at high switching frequencies. These switching devices are not ideal, however, and generate a considerable amount of switching losses that reduce converter efficiency. The main cause of these losses is the overlap of voltage and current that occurs whenever a switch transitions from being fully on or fully off, or vice versa. A significant reduction of these switching losses can be achieved if a converter switch is made to operate with “soft‐switching,” with switching transitions that are gradual as opposed to sudden or hard. Soft‐switching methods can generally be classified as being either zero‐voltage switching (ZVS) with the switch voltage made to be zero during a switching transition or zero‐current switching (ZCS) with the switch voltage made to be zero during a switching transition. Making either the voltage or current of a switch zero during a switching transition ensures the reduction of any overlap of voltage and current and thus the reduction of switching losses. In this article, the basic principles of soft‐switching for dc‐dc power converters operating with high switching frequencies (>50 kHz) are reviewed. A number of soft‐switching methods for simple single‐switch converters such as the boost and buck converters are presented along with methods for more sophisticated dc‐dc converters such as the forward, flyback, and full‐bridge converters. Each method is described in some detail and its strengths and weaknesses are discussed. The article also presents some brief discussion of soft‐switching methods for ac‐dc converters and dc‐ac inverters as well.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".