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Record W1953148706 · doi:10.1109/tdc.2006.1668467

Process and Modeling of Arc on a Snow-Covered Insulator

2006· article· en· W1953148706 on OpenAlexaff
Hamid Javadi, H. Hemmatjou, M. Farzaneh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaHydro-QuébecUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsSnowArc flashInsulator (electricity)Materials scienceVoltageEnvironmental scienceElectrical conductorComposite materialElectrical engineeringMeteorologyEngineeringPhysics

Abstract

fetched live from OpenAlex

Snow and ice accumulation on high voltage equipment such as insulators and conductors may cause problems of mechanical and electrical origins. One of the most serious problems under snow and ice accumulation is insulator flashover, which has been studied to some extent by researchers in several cold climate countries. In this paper, the AC flashover process and electrical behavior of snow deposited on a polymer insulator is the subject of study. Moreover, a mathematical model for simulating the behavior of snow under alternating voltage is presented. For this, two experimental setups were developed and from the voltage-current characteristics of snow, which were measured from several different tests, it was found that the voltage across snow and the leakage current flowing through the snow-covered insulator are almost in the same phase, which it means that a snow-covered insulator behaves as a pure resistance. The resistance of snow is not linear, as it decreases as voltage increases. An increase in length of the snow cover results in an increasing in flashover voltage, but increasing the density and conductivity of water melted from snow yields the inverse effect and causes a sharp decrease in flashover voltage

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.214

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 head0.210
Teacher spread0.200 · 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 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

Citations3
Published2006
Admission routes1
Has abstractyes

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