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Record W2415883948 · doi:10.1109/tdei.2015.005148

Calculation of minimum approach distances for tools for live-line working under freezing conditions

2016· article· en· W2415883948 on OpenAlexafffundabout
Mona Ghassemi, M. Farzaneh

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2016
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaSaskPowerManitoba HydroHydro-QuébecUniversité du Québec à Chicoutimi
KeywordsArc flashCold climateEnvironmental scienceLine (geometry)Margin (machine learning)MoistureVoltageNuclear engineeringReliability engineeringSimulationMeteorologyComputer scienceEngineeringElectrical engineeringMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

IEEE Std. 516-2009 and IEC 61472-2013 present Minimum Approach Distance (MAD) requirements for tools to perform live maintenance safely, provided that the tool has no continuous film of moisture or measurable contamination present on its surface. However, four separate "clean" FRP hot-stick flashover incidents occurred in Canada under steady-state system conditions at the peak of the voltage negative half-cycle during cold and freezing conditions. This paper investigates IEC and IEEE methods of calculating MAD for tools employed for live working done on the geometry of the Manitoba site incidents at 500-kV. Based on cold fog tests carried out at CIGELE to reproduce these incidents, guidelines for calculating MAD in cold and freezing climate regions are proposed.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.054
GPT teacher head0.285
Teacher spread0.231 · 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 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

Citations25
Published2016
Admission routes3
Has abstractyes

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