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On the Gassing Behaviour of Synthetic Ester Under Arcing Faults

2023· article· en· W4384344827 on OpenAlexaff
Moïse T. Agouassi, U. Mohan Rao, Yazid Hadjadj, I. Fofana

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsNational Research Council CanadaUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsFault (geology)Electric arcIntensity (physics)Degradation (telecommunications)ChemistryMaterials scienceElectrical engineeringEngineeringElectrodePhysicsPhysical chemistryGeology

Abstract

fetched live from OpenAlex

In this work, non and aged synthetic esters have been subjected to electrical arcing faults having different intensities. Open beaker thermal aging is performed at $125 ^{\circ}\mathrm{C}$ while the monitoring intervals include 250-aged, 500, 750, and 1000 hours. Arcing fault intensity is realized by performing a repeated number of flashovers in the bulk of the liquid. This includes 30, 60, 90, 120, and 150 repeated breakdowns followed by immediate DGA measurements. This allowed an understanding of the fault gassing behavior at different aging durations and different fault intensities. The degree of degradation is monitored by prominent aging markers, acidity, and interfacial tension in each case. The discussions include digesting the impact of fault gas with degradation at different fault levels and potential correlations for synthetic esters. Appropriate Duval triangle and Duval pentagon methods are used to verify the fault gas diagnostic prediction ability. The results obtained from this study showed that the intensity of the gassing tendency evolves with the degradation rate and is attributable to the intensity of the arcing fault.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.152

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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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 designSimulation or modeling
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

Citations1
Published2023
Admission routes1
Has abstractyes

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