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Preliminary Investigations on the Gassing Tendency and Breakdown strength of Low Pourpoint Transformer Liquids under Selective Conditions

2022· article· en· W4287850215 on OpenAlexaff
Jayasree Thota, U. Mohan Rao, I. Fofana, Patrick Picher, Stephan Brettschneider

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsHydro-QuébecUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMineral oilMaterials scienceTransformerTransformer oilComposite materialFlash pointWater saturationLiquid waterSaturation (graph theory)ChemistryMetallurgyThermodynamicsOrganic chemistryElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

The overall performance of ester liquids is comparable to mineral insulating oils with a reduced environmental impact. However, the application of ester fluids in cold regions is still a challenge to the utilities and transformer owners. In this paper, the water behavior of three low pour point (less than −50°C) insulating liquids (two synthetic esters and a mineral oil) is reported. Non-aged and aged liquid samples underwent electrical discharges of 7 kV (as per a modified ASTM D6180) for 5 hours to understand the gassing tendency and change in water content in the liquid under corona discharges. Also, the water uptake rates of non-aged low pourpoint liquids have been verified at 60°C. Further, non-aged liquids with three different initial water contents have been subjected to 20 repeated AC breakdowns to understand the influence of water on their breakdown failure probability. The obtained results indicate that the gassing tendency under electrical stress of the investigated liquids increases with aging. Similar to the typical ester liquids, the two low pour point ester liquids have higher water saturation limit than that of the mineral oil. In addition, the breakdown failure rates of the low pour point liquids increase with increase in water content.

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 score0.366
Threshold uncertainty score0.329

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 head0.010
GPT teacher head0.211
Teacher spread0.201 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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