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Record W2011254667 · doi:10.1109/tdei.2007.344615

Effect of insulator profile on aging performance of silicone rubber insulators in salt-fog

2007· article· en· W2011254667 on OpenAlexaff
Ayman El‐Hag, Shesha Jayaram, E.A. Cherney

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2007
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSilicone rubberInsulator (electricity)Materials scienceAccelerated agingComposite materialElectric fieldConductivityElectrical engineeringForensic engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

The paper presents the results of the study on the influence of insulator profile on the aging performance of silicone rubber (SIR) insulators in salt-fog. Experiments have been conducted on various two-shed arrangements at 35 V/mm average stress and 0.25 S/m salt-fog conductivity level. The work is also extended to include commercial 15 kV class insulators with different profiles. Shed spacing, shed diameter, alternate shed design and shed shape are the parameters investigated in this study. The low frequency harmonics of the leakage current, early aging period (EAP), and equivalent salt deposit density (ESDD), are used to evaluate the aging performance of different designs. Insulator profile is shown to greatly influence the aging performance of SIR insulators. Shed shape proves to be the most important parameter to be considered in designing non-ceramic insulators profiles. Also, as the shed spacing decreases, the performance of SIR insulators improves. Simulation results using FEMLAB show that the electric field on insulators is below the corona onset at both dry and wet conditions. Dry band arcing is therefore the main electrical cause for aging in distribution class insulators and it is possible to improve the pollution performance of SIR insulators using appropriate profiles as suggested in this work

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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

Citations24
Published2007
Admission routes1
Has abstractyes

Explore more

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