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Record W3118376150 · doi:10.1109/td39804.2020.9300016

Comparison Between IEEE Std C37.41-2016/Cor 1-2017 and IEC 60282-2:2008 Fuse Cutout Interrupting/Breaking Tests

2020· article· en· W3118376150 on OpenAlexaff
Eric Qian Li, C.P. Morton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical Fault Detection and Protection
Canadian institutionsPowertech Labs (Canada)
Fundersnot available
KeywordsFuse (electrical)Reliability engineeringFault (geology)EngineeringTransformerElectrical engineeringComputer scienceVoltageSeismology

Abstract

fetched live from OpenAlex

High voltage fuse cutouts have been used by utilities for close to 100 years, and provide an inexpensive and reliable method of fault protection for the distribution system. Modern distribution cutouts are designed to operate across a full range of interrupting current levels, and are tested to IEEE Std. C37.41-2016/Cor 1-2017 for North America or IEC Std. 60282-2:2008 for Europe and Asia. Before 2016 the interrupting or breaking tests in the IEEE and IEC Standards were very similar. With the revision of IEEE Std. C37.41 in 2016/2017, the interrupting tests in the IEEE Standard are now significantly different than the breaking tests in the IEC Standard. Specifically, the tests for transformer secondary fault current levels (test series/duty 4) have changed considerably compared to the tests in both the superseded 2008 version of IEEE Std. C37.41 and the active version of IEC Std. 60282-2. These differences may have an impact on the performance of existing cutout designs when testing to IEEE Std. C37.41-2016/Cor 1-2017.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.056
GPT teacher head0.310
Teacher spread0.253 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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