Comprehensive Analysis of Buck Mode Frozen Leg Operation for Three-Phase Dual Active Bridge Converters
Bibliographic record
Abstract
The three-phase dual-active-bridge converter exhibits an inherent fault-tolerant capability for addressing open-circuit failures (OCFs). The frozen leg method is a notable fault-tolerant technique that mitigates OCFs without additional hardware. Upon OCF detection, this method deactivates the two switches in the faulty leg, enabling the converter to maintain operation at a reduced power level. However, all previous research on the frozen leg method assumes unity voltage gain. Yet, in practical applications, nonunity voltage gain, such as buck operation, is often unavoidable, wherein the voltage, current, and power characteristics significantly deviate from those at unity voltage gain. Thus, this article performs the first investigation of the more complex buck mode of frozen leg operation, and finds the operation must be categorized into three cases. The theoretical analysis derives the voltage, current, and power expressions for these cases, revealing significant deviations from those associated with unity voltage gain. Based on the derived current expressions, a detailed soft-switching analysis for the buck mode is also conducted. Furthermore, the theoretical maximum transferred power in buck mode under the frozen leg operation is proposed, and unique findings for power transfer at zero phase shift are examined. The theoretical analyses are validated through extensive experimental testing.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".