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Record W4404469643 · doi:10.1109/tpel.2024.3489618

An Open-Circuit Fault-Tolerant Scheme for Single-Phase Current-Fed Dual-Active-Bridge DC/DC Converter

2024· article· en· W4404469643 on OpenAlexafffund
Yue Zhang, Nie Hou, Zheng Wang, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence Fund
KeywordsCurrent (fluid)Dual (grammatical number)Electrical engineeringHalf bridgeThree-phaseFault current limiterBridge (graph theory)Control theory (sociology)EngineeringComputer scienceElectronic engineeringPhysicsCapacitorVoltagePower (physics)Electric power systemControl (management)

Abstract

fetched live from OpenAlex

Power interruptions are intolerable in many industrial applications, so high reliability becomes exceedingly pivotal to power converters. Fault-tolerant schemes attract more attention as an effective approach to improve reliability. However, due to the low-device-redundancy and asymmetrical structure, fault-tolerant schemes have not yet been reported on the single-phase current-fed dual-active-bridge (CF-DAB) converters. To address this challenging issue, an open-circuit fault-tolerant scheme is proposed in this article. With the proposed fault-tolerant scheme, the single-phase CF-DAB converter can maintain power delivery even if facing the open-circuit faults of two specific switches and the middle capacitor. Meanwhile, potential voltage spikes can also be suppressed effectively without the snubber function of the middle capacitor, which protects the switching devices from high-voltage stresses. Subsequently, the modulation scheme, mode characters, and control structure are elaborated. Finally, the experimental results based on a scale-down laboratory prototype are presented to verify the feasibility of the proposed fault-tolerant scheme.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.302
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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