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Effect of binding wire on electric field distribution of overhead insulated conductor in distribution network

2020· article· en· W3115488711 on OpenAlexaff
Yuanpeng Liang, Nianwen Xiang, Kejie Li, Kai He, Jin Yang, Ye Tian, Lijun Zhou

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsSR Research (Canada)
Fundersnot available
KeywordsConductorElectric fieldElectrical conductorMaterials scienceInsulator (electricity)Electrical engineeringIntensity (physics)MechanicsComposite materialCondensed matter physicsEngineeringOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract In recent years, the ablation damage of overhead insulated conductor on the top of insulators has posed a threat to the safe and stable operation of distribution network. In this paper, a three-dimensional model including insulator, binding wire and conductor is established in the finite element simulation. Then analyse the influence of the existence of binding wire and the diameter of binding wire on the electric field distribution of conductor. Providing a theoretical basis for the cause and prevention of the ablation damage of conductor insulation layer. The simulation results are as follows: When binding wire exists, electric field distortion will occur in the insulation layer and surface air around the conductor. The maximum electric field intensity of the surface air around conductor reaches 15.5kV/cm, which is about 690% higher than that without a binding wire. The maximum electric field intensity of the insulation layer reaches 4.3kV/cm, which is 980% higher than that without binding wire. In addition, as the diameter of the binding wire increases, the maximum electric field intensity in the insulating layer decreases and the maximum electric field intensity in the air on the surface around the conductor has small change.

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 score0.055
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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