Accurate Light Load Loss Analysis of Hybrid Modulation Strategy for ZVS Operation of Low-Q LLC Resonant Converter for Wide Input Voltage Range Applications
Bibliographic record
Abstract
Light load efficiency and output regulation of LLC resonant converter is a critical problem for wide input voltage and load range applications due to converter's parasitic capacitances such as rectifier diode junction capacitance (Cj). Compact size, high density and high transformer turns-ratio requirements for micro-inverter applications adds significant distributed capacitance (Cd) of low-profile transformer, worsening output regulation and zero- voltage switching (ZVS) capability at light loads. Magnetic core losses and turn-off switching losses in power MOSFETs further degrade power conversion efficiency at light loads. Therefore, an improved loss analysis for a hybrid modulation technique is proposed for full-bridge LLC resonant converter. The proposed methodology calculates duty cycle offline such that minimum power losses are incurred at different light loading condition. Variation in switching frequency at selected duty cycle value regulates output voltage. Time domain analysis of proposed technique including effects of Cdand Cjis performed to ensure accurate loss calculation. An experimental prototype for 20-40V input, 380V/300W output LLC converter is tested for validation of theoretical analysis.
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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 | 0.001 |
| Open science | 0.001 | 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".