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Record W3193624317 · doi:10.1109/tdei.2021.009452

How Temperature and Pressure Affect the Electric Field Distribution in HVDC GIS/GIL: A Numerical Study

2021· article· en· W3193624317 on OpenAlexfundno aff
Yi Luo, Ju Tang, Zijun Pan, Cheng Pan

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2021
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaCanada School of Energy and Environment
KeywordsElectric fieldSwitchgearMechanicsConvectionThermal conductionCharge densitySurface chargeThermalMaterials scienceChemistryPhysicsThermodynamicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

In this paper, an electro-thermal based surface charge accumulation model is established. Heat conduction, heat convection and heat radiation are included. The gas flow is coupled with fluid equations to describe the motion of charge carriers, and gas current density is also corrected by the gas velocity. In terms of it, the surface charge and electric field on a basin-type insulator in vertical laid DC gas insulated switchgear (GIS) and gas insulated transmission line (GIL) under different temperatures and pressures are studied. The results show that a temperature gradient is formed in the gas from top to bottom in the vertical direction, and the increase in pressure leads to a higher temperature of the lower surface. The surface charge density and tangential electric field increase with the temperature and decrease with the pressure. As a comparison, the surface charge and tangential electric field are calculated without considering temperature and without considering heat convection. The former leads to a lower tangential electric field than this model, while the latter brings about a higher electric field. This paper provides a more accurate reference for the design of DC GIS/GIL through a more comprehensive model.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.243
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 teacher head, 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

Citations30
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Dielectrics and Electrical InsulationSame topicHigh voltage insulation and dielectric phenomenaFrench-language works237,207