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Correlation between the Optical Properties and the Degree of Polymerisation of Transformer Insulation Paper

2023· article· en· W4384302257 on OpenAlexaff
N. Seiffadini, Bekibenan Sékongo, F. Meghnefi, Kok‐Sing Lim, O. C. Weng, Waldo Udos, U. Mohan Rao, I. Fofana, A. Cherhi, Mohand Ouhrouche

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsRoyal Military College of CanadaUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsTransformerCorrelationDegree (music)Materials sciencePolymerizationComposite materialElectrical engineeringEngineeringPhysicsMathematicsVoltageAcousticsPolymer

Abstract

fetched live from OpenAlex

Transformer’s insulation system consists of oilpaper insulation, which is the most critical component that largely influences its service life. In service, the degradation of the transformer oil-paper insulation is inevitable, even under normal conditions. The degree of polymerization (DP) is a widely accepted and direct technique to assess the deterioration of the paper insulation. However, once a transformer is commissioned and energized, the direct assessment of the condition of the paper insulation is not possible. While insulating oil can be sampled for analyses when needed, this is hardly the case for the solid insulation system (paper). It is therefore, a usual practice to monitor the solid insulation system indirectly. Another incentive is that the life expectancy of a transformer is directly related to the aging condition of its paper insulation. Therefore, the direct degradation monitoring of solid insulation is an interesting approach. In this contribution, an optical aging marker is investigated to monitor the degradation of the paper insulation in oil-filled transformers. The reflectance of the aged papers has been monitored, and the relationship with DP is investigated at three different commercial wavelengths. It is noticed that a correlation between the reflectance spectrum and DP-values may be used to monitor the aging conditions of the paper.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.128

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)0.0000.000

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 head0.036
GPT teacher head0.211
Teacher spread0.174 · 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 designObservational
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

Citations2
Published2023
Admission routes1
Has abstractyes

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