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Record W3084335619 · doi:10.1115/es2020-1716

Effect of Waste Vegetable Oil on Cooling Performance and Lifetime of Power Transformers

2020· article· en· W3084335619 on OpenAlexaff
Ehsan Ebrahimnia-Bajestan, Mohammad Arjmand, Hani Tiznobaik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMineral oilTransformerTransformer oilCoolantEnvironmental scienceWaste managementVegetable oilTransformer effectMaterials scienceDistribution transformerElectrical engineeringEngineeringMechanical engineeringChemistryVoltageMetallurgy

Abstract

fetched live from OpenAlex

Abstract During the operation of a power transformer, a large amount of heat is generated due to the electrical and magnetic energy losses in its core and windings, causing a temperature rise in transformers. This generated heat is known as the main factor for aging the electrical insulating system of a transformer. In this research, we numerically studied the ability of a vegetable-based oil — as an alternative coolant for the petroleum-based oils — on the cooling performance of a power transformer. The studied oil was a biodiesel produced from waste cooking vegetable oils, having lower viscosity compared to traditional mineral oils. We also calculated the aging rate of the transformer in the presence of the biodiesel. The results indicated that compared to the mineral oil, the average hotspot temperature of the transformer is 3 degrees lower when the biodiesel was used. The life expectancy of the transformer with the vegetable-based oil was also significantly longer than the case with mineral oil. In conclusion, this study provided a sustainable way to use an eco-friendly material produced from a waste resource as an alternative insulating liquid for the cooling of power transformers.

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 score0.309
Threshold uncertainty score0.306

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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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