An Analysis of Advanced Soft-Switching Techniques for DC–AC Power Converters Based on Auxiliary Circuits
Bibliographic record
Abstract
At present, there is a huge trend in the industry for power converters that have the ability to reach a high efficiency at high switching frequencies. General power converters have switching losses which act as a barrier in terms of efficiency while designing high-frequency converters. As a solution for this, soft switching techniques come into play by integrating a resonant circuit into the traditional power converter. These resonant converters give the ability of the power converters to operate in high frequencies without switching losses. Also in electric vehicles as well as wireless charging, the switching frequency can be increased to a higher level without the switching losses using these converters. Furthermore, it can be used in bi-directional converters with MOSFETs like SiC and GaN. An analytical review of the advanced resonant converter design topologies is discussed in this paper based on three main designs for auxiliary-based circuits. Finally, a comparative analysis is conducted using the various converter parameters. This analysis will be a huge benefit for the power electronics circuit design area to select the most suitable type of circuit for the application.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".