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Record W4399146291 · doi:10.1109/tia.2024.3406647

Safety Through Switching Speed

2024· article· en· W4399146291 on OpenAlexaff
John Kay, Philip Allen, Eric Norton

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2024
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsRockwell Automation (Canada)
Fundersnot available
KeywordsComputer scienceElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

This paper expands on the relationship between fault interrupting speed versus incident energy. It will provide some details about the world's fastest low voltage (LV) circuit breaking technology. With ultra-fast and consistent clearing times, devices using this technology provide the fastest method of removing fault currents. This sequentially reduces fault incident energies to nearly non-existent levels. Adding this switching technology could be the next step in enhancing your safety program. This new and very rapidly growing technology is the LV Solid-State Circuit Breaker (SSCB). Using silicon carbide (SiC) semiconductor modules, and unique current sensing methodologies, furnishes the SSCB with the ability to provide extremely rapid, consistent, and arc-less switching. Since the interruption of the current is performed without any electro-mechanical contacts, these devices can provide an almost unlimited number of switching operations. While at the same time, release virtually no arc flash incident energy whilst also limiting the let-through current to the lowest level in industry. In this work, we will provide an overview of this new technology and how Petroleum and Chemical industry users can take advantage of eliminating almost all release of arc flash incident energy associated with traditional LV molded case circuit breakers (MCCB). And, at the same time, significantly reduce the electromechanical stress on the connected load while virtually eliminating the need for breaker maintenance providing a protection device with a much higher mean time between failures.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.016
GPT teacher head0.258
Teacher spread0.242 · 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 routes1
Has abstractyes

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