Variable Switching Frequency Modulation for Wide ZVS Range in Y-Inverter Using Adaptive Deadtime
Bibliographic record
Abstract
Increasing the efficiency in buck-boost Y-voltage source inverter (Y-VSI) can happen by reducing the switching losses. This paper proposes a new soft-switching method to achieve minimum switching losses in Y-VSI. First, switching inductor current boundaries are found using the current equation in both buck and boost chopping operation. To minimize the required deadtime for capacitor discharging between turn on and turn off of the switches, an adaptive deadtime is calculated for both lower and upper boundaries of the inductor current. Using calculated deadtime for turn on and turn off in the switches, variable frequency pulse width modulation (PWM) signals are used to achieve full zero voltage switching (ZVS) range for both buck side and boost side switches in Y-VSI. Simulation results verify that the proposed ZVS method increases efficiency more than %2 in a 5 kW SJ-based Y-VSI.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".