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 (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> ). Compact size, high density and high transformer turns-ratio requirements for micro-inverter applications adds significant distributed capacitance (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> ) 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 C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> is 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".