Short-Circuit Fault Diagnosis and Post-Fault Control with Adaptive PLL-Based Synchronization for a Multi-Phase Quasi-Square-Wave DC-DC Converter
Bibliographic record
Abstract
Fault-tolerant power management units have regained attention due to the rapid development of autonomous vehicles. This paper presents: 1) a fast short-circuit fault diagnosis method, which prevents turning on into a short-circuit, and 2) an adaptive post-fault control for a multi-phase variable-frequency Quasi-Square-Wave (QSW) synchronous buck converter with more than two phases. The converter employs GaN device as the primary switch due to its better figure-of-merit, while using Si device as protection switch due to superior short-circuit immunity. In this paper, the QSW operation mode and a multi-phase structure are combined to achieve enhanced efficiency and fault tolerance. The switching-node voltage during the dead-time intervals is used as the short-circuit fault signature. The adaptive post-fault control utilizes a PLL-based synchronization method in a closed daisy-chain arrangement to: 1) guarantee QSW operation mode regardless of the variable switching frequency due to the inductance value tolerance, and 2) automatically adjust the interleaving phase shifts between phases following a fault detection. The effectiveness of the proposed methods is evaluated and verified in a 75-W four-phase system.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".