Boost Mode Analysis and ZVS-Guaranteed Control Strategy for Frozen Leg Operation of Three-Phase Dual Active Bridge Converters
Bibliographic record
Abstract
The frozen leg method is an effective fault-tolerant approach for addressing open-circuit faults in three-phase dual active-bridge converters, requiring no additional hardware. This method isolates the faulty leg by disabling its two switches so that the converter can continue operating at reduced power levels. Prior literature on the frozen leg method primarily focuses on cases with unity voltage gain or buck mode. However, in boost mode where a higher voltage difference must be overcome, the faulty phase may enter discontinuous current mode, introducing additional complexity and necessitating a new mathematical framework. Thus, this paper presents the first comprehensive investigation of boost mode frozen leg operation, proposing a novel analytical framework for the discontinuous current mode. The analysis of boost mode is divided into five distinct cases, with corresponding derivations for power, current, and voltage equations. The findings show significant differences in operation compared to buck and unity gain modes. Additionally, this research derives the maximum power transfer achievable in boost mode and conducts a detailed soft-switching analysis. Based on this analysis, a ZVS-guaranteed control strategy is proposed to ensure that the operating points remain within the high-efficiency ZVS region. Experimental results are provided to validate the theoretical analysis.
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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 | 0.000 |
| 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".