Current-Sharing Worst-Case Analysis of Three-Phase <i>CLLC</i> Resonant Converters
Bibliographic record
Abstract
Three-phaseCLLCresonant converters provide higher power conversion capability as compared to half-bridge and full-bridge structures. In addition to the unique features ofCLLCconverters for bidirectional applications, the three-phase structure provides significantly reduced output current ripple (smaller output capacitor), parallel power processing (reduced components size and current peak stress), and better thermal distribution (smaller heatsinks). However, with practical, i.e., nonzero, resonant component tolerances, these benefits are normally less, and sometimes significantly less than expected in the ideal case. In this article, the unbalanced behavior of the converter with 15 unknown resonant components is identified and analyzed. A new analysis methodology is proposed to investigate the worst-cases of current-sharing among above 32 000 possible scenarios in three-phaseCLLCresonant converters. In addition, this article shows that phase-shifting techniques can be effective to mitigate the unbalanced behavior of the converter. The proposed analysis in this article helps to determine the highest admissible tolerance in the components to keep the converter working within a certain range of unbalanced behavior without requiring any balancing techniques. The proposed analytical framework is verified with experimental and simulation results of a 3-kW bidirectional three-phaseCLLCexperimental prototype.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".