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Record W1989713127 · doi:10.1109/tdei.2013.003999

Improved dynamic model of DC arc discharge on ice-covered post insulator surfaces

2014· article· en· W1989713127 on OpenAlexafffund
Shamsodin Taheri, M. Farzaneh, I. Fofana

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2014
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaHydro-QuébecUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec à Chicoutimi
KeywordsArc flashInsulator (electricity)IcingVoltageMaterials scienceMechanicsIcing conditionsElectric arcAir gap (plumbing)Electrical engineeringEngineeringMeteorologyPhysicsOptoelectronicsComposite materialElectrode

Abstract

fetched live from OpenAlex

This paper presents a self-consistent dynamic two-arc model predicting the behavior of DC arcs on ice-covered station post insulators. This model makes it possible to determine the minimum flashover voltage (VMF) as well as some characteristics of the discharge, including temporal evolution of leakage current, arc characteristics and icing severity of the insulators. The model takes into account the variation of freezing water conductivity, insulator geometry, ice layer characteristics, applied voltage polarities and some fundamental concepts of air gap formation. The model was validated in laboratory using a typical 735-kV ice-covered station post insulator under DC voltage. The VMF was experimentally determined based on IEEE Std 1783. Moreover, the influence of the number and position of air gaps on the minimum flashover voltage was investigated experimentally and mathematically. There was a good concordance between the experimental results and those predicted by the model. This model is a good foundation for the development of multi-arc models and a powerful tool for the design and selection of DC EHV insulators subjected to ice accretion.

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 categoriesMeta-epidemiology (narrow)
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 score0.785
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
Bibliometrics0.0010.001
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 head0.013
GPT teacher head0.234
Teacher spread0.222 · 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 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

Citations31
Published2014
Admission routes2
Has abstractyes

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