Interleaved SCC-LCLC Converter with TO-220 GaN HEMTs and Accurate Current Sharing for Wide Operating Range in Data Center Application
Bibliographic record
Abstract
In this paper, an interleaved LCLC converter with TO-220 enhancement mode GaN devices (e-mode GaN) and accurate current sharing performance is introduced for data center application. Any tolerance in the resonant tank elements can lead to large load imbalance between different phases. Due to the steep gain curve of LCLC converter, conventional current sharing methods are not so effective. In the proposed converter the impedances of the resonant networks are matched by switching a capacitor (i.e. Switch Controlled Capacitor SCC) in series with the resonant capacitor in one or some of the phases, which results in accurate load current sharing between phases (i.e. around 0.025% difference). The load share of each phase is sensed through the resonant current on each phase and the control logic is applied so current sharing between all phases can be achieved. By this method, an accurate current sharing is achieved for a wide input voltage range that is required for hold up time in data center application. Moreover, interleaving is applied in the proposed multi-phase LCLC converter resulting in low stress on the output capacitor allowing sole ceramic capacitor implementation. Moreover, phase shedding allows a flat high conversion efficiency curve for a wide load range. The performance of the proposed interleaved LCLC converter is verified by a two-phase 1 kW prototype with 250 V - 400 V input voltage and fixed 12 V output voltage.
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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".