Unbalanced Three-Phase <inline-formula> <tex-math notation="LaTeX">$LLC$</tex-math> </inline-formula> Resonant Converters: Analysis and Trigonometric Current Balancing
Bibliographic record
Abstract
Three-phase LLC resonant converters can handle very high power levels beyond the capabilities of half-bridge and full-bridge LLC topologies. Among other characteristics, three-phase LLC structures reduce output current ripple (small output filter), enable parallel power processing (low peak current), and provide good thermal distribution. However, all these key advantages can be severely compromised due to passive components tolerances, leading to undesired current balance issues in three-phase LLC resonant converters. Tolerances in resonant tank passive components are inevitable and lead to unequal peak currents between phases, uneven temperature distribution, and large output current ripple. This paper investigates the imbalances in three-phase LLC converters and proposes a novel trigonometric current balancing (TCB) technique using phasor analysis. In this strategy, the required input voltage phase angles are calculated to achieve balanced phase currents, even under severe unbalanced conditions. In some cases, the output filter current ripple is reduced to less than half. The methodology is verified with a 3-kW experimental prototype, which validates the analytical framework and effectiveness of TCB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".