Full Range Analysis and Application of Frozen Leg Operation for 3-Phase DAB Converters With Open-Circuit Failure
Bibliographic record
Abstract
The three-phase dual-active-bridge (3p-DAB) converter possesses inherent fault-tolerant capability to address open-circuit failures (OCFs). The frozen leg method tackles OCFs without additional hardware by disabling the two switches in the faulty leg, allowing the 3p-DAB to transfer reduced power, generally by maintaining the original phase shift angle. However, prior literature on the frozen leg method focused only on operation with phase shift angles from 0 to π/3. The power transfer characteristics beyond this range, specifically from π/3 to π/2, have not been explored in the literature. To address this gap, this paper derives novel equations to describe the transferred power of a 3p-DAB operating with frozen leg control over the phase shift range of π/3 to π/2. These equations show for the first time that the theoretical maximum power transfer during frozen leg operation is 75.6% of the normal operation maximum power transfer. Further, based on the derived curves, this paper proposes a method to increase power transfer of the frozen leg method by dynamically adjusting the phase shift angle, which would not be possible without the full power transfer curve from 0 to π/2. The soft-switching analysis for the proposed method is also presented. The theoretical analysis and proposed method 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 | 0.001 |
| 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".