A ZVT cell for high-frequency quasi-resonant converters in ON-OFF mode for solar applications
Bibliographic record
Abstract
In this paper, a fully ZVT auxiliary cell for boost type resonant converters is proposed to achieve soft switching in ON-OFF control mode. In conventional ON-OFF methods, transient and switching losses at the beginning and end of each power pulse decrease the overall efficiency of the system at high modulation frequencies. This ZVT cell offers zero voltage switching without imposing extra resonant current. This structure can be implemented in most boost type resonant converters such as class E, φ2and quasi-resonant DC-DC converters. The fully ZVS operation, the absence of inductive components, and low voltage and current stress are main advantages of this cell. Thus, the ON-OFF control can be implemented at higher modulation frequencies without any significant drop in the efficiency. Simple structure and timing are other advantages of the proposed cell, which makes it useful for solar applications. The operation of the proposed auxiliary cell is analyzed in individual intervals. To verify the mathematical analysis, the proposed cell is implemented in a 3 MHz quasi-resonant boost converter. A maximum efficiency of 95.8% is obtained at 385 watts output and the efficiency drops about two percent at 80 watts at 500 kHz modulation frequency.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".