A Time-Domain Modeling of Multi-Element Resonant Converter With Capacitive Output Filter
Bibliographic record
Abstract
In this thesis, an analytical based design tool has been proposed for the multi-elements resonant converters. A computer program has been written using the generalized equations in MATLAB App Designer. The MATLAB App Designer provides a graphical user interface (GUI) features to this tool. The time-domain analysis of CLL resonant converter has been introduced in the literature. An alternative time-domain analysis of LLC resonant converter has been proposed.This generalized analysis is used to model LLC resonant, CLL resonant and LC series resonant converter in time-domain. State-plane analysis has been also introduced for both LLC and CLL resonant converters. The performance curves for DC power, voltage gain, peak switch current, RMS value of switch current, peak capacitor voltage and zero voltage switching (ZVS) angle are presented as a function of the frequency and the load. State-of-the-art design examples have been shown. This work also proposes a novel Push-Pull resonant converter. The CLL resonant tank has been find to be best fit for secondary side resonance with push-pull configuration. A steady-state analysis of Push-Pull CLL resonant converter has been done in time-domain. The CLL resonant tank has been designed to provide both series and parallel resonance characteristic based on proper selection of inductance ratio and quality factor. A computer program has been written using the generalized equations in MATLAB App Designer for study and optimization of CLL resonant tank. Experimentation has been done to verify the soft-switching features of Push-Pull resonant converter. A comparative analysis is shown between LLC and CLL resonant tanks using design curves. Experimentation has been done to verify the theoretical findings from this tool on the experimental prototype of 400V/320W for LLC and CLL resonant converters operating between input voltage 20V-40V.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".