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Record W2897065717 · doi:10.1049/iet-gtd.2018.6077

Comparative study of the degradation rate of new and regenerated mineral oils following electrical stress

2018· article· en· W2897065717 on OpenAlexaff
Leila Safiddine Oumert, Ahmed Boucherit, Amel Hadj‐Ziane‐Zafour, I. Fofana

Bibliographic record

VenueIET Generation Transmission & Distribution · 2018
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsDegradation (telecommunications)Mineral oilDielectricElectrical resistivity and conductivityTransformer oilMaterials scienceDissipation factorDielectric lossTransformerAcid valueElectric fieldEnvironmental scienceComposite materialAnalytical Chemistry (journal)Petroleum engineeringChemistryElectrical engineeringEnvironmental chemistryGeologyVoltageMetallurgyOptoelectronics

Abstract

fetched live from OpenAlex

The objective of this contribution was to study the behaviour of new and regenerated insulating oil used in power transformers under the influence of an electric stress. To estimate the degradation rate of the dielectric fluids, one thousand (1000) successive breakdowns were generated according to the IEC 60156 standard. The parameters such as dissipation factor (Tan δ ), resistivity, total acid number (TAN) and oil water content were measured and examined following IEC/ISO standards. Good correlations have been obtained between TAN/resistivity and Tan δ which might provide a ‘picture’ of the fluid condition. The dissolved oxidation products for the two dielectric fluids (after the application of electric breakdowns) was evaluated by Fourier‐transform infrared spectroscopy. The results obtained indicate that the degradation of the parameters is significant and confirms the influence of an alternative electric field (AC) on the new and regenerated oils. It was also suspected that inhibitors and antioxidants were removed from the oil after regeneration. Their concentration should therefore be monitored and replenished when necessary.

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 score0.208
Threshold uncertainty score0.438

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 head0.023
GPT teacher head0.257
Teacher spread0.234 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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