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Record W2144604832 · doi:10.1109/elinsl.1996.549445

Aging of XLPE cable insulation under combined electrical and mechanical stresses

2002· article· en· W2144604832 on OpenAlexaff
Éric David, J.-L. Parpal, J.‐P. Crine

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsElectrical treeingMaterials scienceElectric breakdownElectrical engineeringComposite materialEngineeringPartial dischargeVoltageDielectricOptoelectronics

Abstract

fetched live from OpenAlex

Extruded crosslinked polyethylene (XLPE) insulation is widely used in high-voltage cables since it presents such attractive features as excellent dielectric properties and good thermomechanical behavior. However, its performance is affected by long-term degradation when it is subjected to the various thermal, mechanical and environmental stresses occurring in service in combination with electrical stress. The synergetic effect of superposed electrical and other stresses remains to be fully clarified. In particular, a fairly high level of mechanical stresses can be present in the insulation volume originating from residual internal stresses created during the cooling process in the fabrication, external forces when cables are bent sharply, or thermomechanical stresses caused by differential thermal expansion between the conductor and the insulating material. In order to investigate the influence of the superposition of mechanical and electrical stresses, various measurements were conducted on XLPE and LDPE specimens in tip-plane and plane-plane geometries. Experimental data of time to-breakdown, breakdown field and tree length are presented as a function of the magnitude of the stresses. In all cases, superposition of the mechanical stress was round to reduce the dielectric strength of the material.

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 categoriesInsufficient payload (model declined to judge)
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 score0.325
Threshold uncertainty score0.999

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)0.0020.000

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 head0.021
GPT teacher head0.232
Teacher spread0.211 · 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

Citations15
Published2002
Admission routes1
Has abstractyes

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