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Record W4394711326 · doi:10.1109/tdei.2024.3387411

Effects of Repetitive Fast Transients on Aging of Transformer Insulation

2024· article· en· W4394711326 on OpenAlexafffund
Anurag A. Devadiga, Shesha Jayaram

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2024
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransformerMaterials scienceAccelerated agingElectrical engineeringElectronic engineeringEngineeringVoltageComposite material

Abstract

fetched live from OpenAlex

The wind-turbine step-up transformers are subjected to high-frequency high dV/dt transient voltages due to the operation of power electronic converters and vacuum circuit breakers. These high-frequency transients are harmful to the transformer turn-to-turn and layer-to-layer paper-oil insulation. The current research work addresses the effect of the transient voltage parameters on the degradation of the transformer paper-oil insulation. Two-level, two-factor design of experiments were used to obtain the effect of transient voltage parameters on the ageing of the paper-oil insulation. Rise time and switching frequency were the two factors (parameters) selected for the ageing experiments. The two values of rise time were 220 ns and 650 ns, and two values of the switching frequency were 1 kHz and 3 kHz. The faster rise time (220 ns) and higher switching frequency (3 kHz) of the voltage led to a higher degradation for the paper-oil insulation. Additionally, the transient voltage ageing of paper-oil insulation is investigated and compared for two types of mineral oils: Luminol™ TRi oil and Voltesso <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> 35 oil, that have differences in their electrical and chemical properties.

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 score0.400
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Dielectrics and Electrical InsulationSame topicHigh voltage insulation and dielectric phenomenaFrench-language works237,207