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Localization of Open-Circuit Faults in GaN-Based Three-Phase Dual Active Bridge Converters with Reduced Sensing Requirements

2024· article· en· W4396593740 on OpenAlexaff
Satyam Sa, Yi Han, Seyed Amir Assadi, Mohammad Shawkat Zaman, Olivier Trescases

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConvertersComputer scienceControl reconfigurationFault detection and isolationProcess (computing)Fault (geology)Electronic engineeringControl theory (sociology)VoltageEmbedded systemEngineeringElectrical engineeringArtificial intelligenceActuator

Abstract

fetched live from OpenAlex

The paper presents a cost-effective method for detecting and localizing single-switch open-circuit faults (OCFs) in three-phase dual active bridge converters using the αβ transformation. The proposed algorithm reduces the number of required current sensors and simplifies the detection and localization processes compared to state-of-the-art methods. The algorithm is verified using an experimental prototype operating at 400 V delivering 2 kW, using 650-V GaN devices switching at 300 kHz. Successful detection of all single-switch OCFs is demonstrated, with a worst-case detection time of 5 switching periods, verifying the effectiveness of the proposed algorithm. An auto-correction process to compensate for dc biases in the detected signals due to variations in system and component parameters is developed, which dynamically optimizes the detection time in different operating conditions. Fast OCF detection, as enabled by the proposed method, allows for the reconfiguration of the converter for post-fault operation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.054
GPT teacher head0.306
Teacher spread0.252 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations2
Published2024
Admission routes1
Has abstractyes

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