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Record W2965156438 · doi:10.1109/tdei.2019.007991

Modified stress grading system for a 13.8 kV inverter-fed rotating machine

2019· article· en· W2965156438 on OpenAlexaff
Alireza Naeini, E.A. Cherney, Shesha Jayaram

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2019
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsVoltageMaterials scienceElectric fieldElectrical engineeringPartial dischargeElectrical conductorRise timeComposite materialStress (linguistics)PhysicsEngineering

Abstract

fetched live from OpenAlex

Evaluation of thermal and electrical characteristics of a 13.8 kV rotating machine stress grading system shows that a reduced length of conductive armor tape (CAT) from the slot exit reduces the electric field and temperature rise under pulsed voltage. Although, the electric field in stress grading tape (SGT) increased slightly, with a shorter length of CAT during the rise time of the pulse voltage, it decreased during the DC portion of the pulse voltage. The temperature profile of the stress grading system under pulsed voltage at room, elevated room, and near typical operating temperatures are measured and simulated for several CAT lengths; results show lower temperatures for shorter CAT length. The partial discharge inception voltage is unchanged for shorter CAT lengths. A lower temperature rise is desirable as this leads to a longer life in the absence of partial discharges.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.016
GPT teacher head0.219
Teacher spread0.203 · 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 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

Citations14
Published2019
Admission routes1
Has abstractyes

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