MétaCan
Menu
Back to cohort
Record W4249204401 · doi:10.1109/pcicon.2016.7589211

Applying arc resistant technologies to medium voltage variable frequency drives

2016· article· en· W4249204401 on OpenAlexaff
Richard Paes, John Kay, Brandon Cassimere

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical Fault Detection and Protection
Canadian institutionsRockwell Automation (Canada)
Fundersnot available
KeywordsSwitchgearCircuit breakerEngineeringVoltageElectric arcElectrical engineeringArc-fault circuit interrupterArc (geometry)High voltageAutomotive engineeringComputer scienceMechanical engineeringShort circuit

Abstract

fetched live from OpenAlex

Arc resistant low/medium voltage switchgear and motor controls continue to provide proven levels of additional personnel safety in most every petroleum and chemical facility. The Institute of Electrical and Electronics Engineers (IEEE) and International Electrotechnical Commission (IEC) generated testing guides and standards for these products which are open to some level of interpretation when applied to products other than traditional medium voltage circuit breaker or fundamental motor controller structures for which the current available standards and guides were intended when they were initially written. All medium voltage variable frequency drives, because of their inherent designs, require high volumes of cooling air which pose a real challenge in regards to providing full arc resistant capabilities and compliance to the present testing guides, procedures, and standards. This paper will deliver real solutions to this problem through the application of unique cooling processes, arc channeling technologies, structural enhancements, arc fault energy limiting methodologies and special application considerations when applying arc resistant medium voltage drives.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicElectrical Fault Detection and ProtectionFrench-language works237,207