A Novel Time Domain Analysis of the LLC-L Resonant Converter for the Use of the CLL and LLC Resonant Converter
Bibliographic record
Abstract
The present work makes an effort to provide an accurate, approximation free time domain analysis of four elements LLC-L resonant converter for the precise modeling of the CLL and LLC resonant converters in high frequency applications. By this, the major contribution of this paper is to introduce the time domain modelling of the CLL resonant converter in the literature. The derived analytical expressions by modeling of LLC-L resonant tank is also useful for modeling of the series LC, parallel CL, series LCL topologies. Closed form solutions are being derived for converter's voltage gain, peak stress, tank RMS current, tank capacitor voltage, zero voltage switching (ZVS) angle etc. as a function of the load, frequency and other circuit parameters. The main feature of this novel time domain analysis has been accurately identifying the zero crossing points of various current flowing through the resonant tank. Analytically generated steady-state waveforms and design curves are verified and are an exact match with PSIM simulation studies. The other contribution of this paper by this way is to generate the accurate steady state waveforms for both LLC and CLL based on developed time domain mathematics. Performance curves are given, design issues are discussed and the resonant tank characteristics are demonstrated on an experimental setup of 320W power, 420V output voltage.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".