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Record W2170605565 · doi:10.1109/tpwrd.2002.803713

Estimating overpressures in pole-type distribution transformers. II. Prediction tools

2003· article· en· W2170605565 on OpenAlexaff
Jean‐Bernard Dastous, Marc Foata, A. Hamel

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2003
Typearticle
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsOverpressureCircuit breakerEngineeringTransformerStructural engineeringDistribution transformerElectrical impedanceFuse (electrical)Geotechnical engineeringMechanical engineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Low-impedance faults are difficult to contain from the point of view of cover retention and tank rupture in pole-type distribution transformers. When evaluating a transformer's containment capabilities, one needs to consider two aspects: the overpressure caused by an arc of given parameters and the pressure withstand of the tank. This paper presents two methods to calculate overpressures as a function of the arc and all of the relevant geometrical parameters: tank diameter, air-space height, and the depth of the arc below the oil. Both methods have been validated with experiments. Results of tests to determine the pressure withstand of typical tanks are presented. It was found that an average value of the overpressure withstand capability is approximately 100 kPa. For a given tank geometry and fault parameters, a current-limiting fuse would be required if the methods proposed here predict an overpressure of more than 100 kPa.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.212
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2003
Admission routes1
Has abstractyes

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