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Record W2547977767 · doi:10.1109/epe.2016.7695379

Power management for self-powered SiC based AC smart-breaker for nano-grid applications

2016· article· en· W2547977767 on OpenAlexaff
Masafumi Otsuka, Klaus David, Olivier Trescases

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCircuit breakerVoltage dropElectrical engineeringPower managementComputer scienceVoltageSmart gridHigh voltageVoltage regulationElectronic engineeringPower (physics)Engineering

Abstract

fetched live from OpenAlex

This work targets a novel self-powered Smart Circuit Breaker (SCB) for monitoring and controlling power in emerging small-scale AC nano-grids. The SCB concept is intended as a direct replacement of standard(120 Vac, 15 Arms) household circuit breakers, which imposes several challenging constraints. The SCB must therefore generate its own internal supply from the small AC voltage drop across the main switch when the breaker is closed. The SCB is composed of the following key blocks: 1) back-to-back 900 V Silicon Carbide (SiC) MOSFETs as the main power switches, 2) a low-voltage energy harvesting circuit, 3) a high-voltage step-down converter, and 4) low-power digital controller and wireless communication circuits. In order to stabilize the internal supply voltage under a wide range of AC load currents, a novel approach of dynamic on-resistance control is implemented, through adaptive gate-drive and MOSFET segmentation. The fabricated SCB prototype dissipates only 7.4 W for a 13 Arms AC load, corresponding to an efficiency of 99.5%.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.793
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.219
Teacher spread0.210 · 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
Published2016
Admission routes1
Has abstractyes

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