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Record W2164016436 · doi:10.1109/elinsl.2008.4570411

Capacitive Grading of 13.8 kV Form-Wound Motor Coil Ends for Pulse Width Modulated Drive Operation

2008· article· en· W2164016436 on OpenAlexaff
Emad Sharifi, Shesha Jayaram, E.A. Cherney

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPulse-width modulationCapacitive sensingElectromagnetic coilVoltageElectrical engineeringMaterials scienceInduction motorDC motorInverterMotor soft starterEngineering

Abstract

fetched live from OpenAlex

The use of solid state drives for medium voltage motors has been growing significantly. Voltage source inverter (VSI) is one of the most popular classes of these motor drives that along with pulse width modulation (PWM) create many capabilities for motor speed and torque control. On the other side, these drives have adverse effects on the insulation system, particularly on the stress grading (SG) tape on form-wound coils. Enhancement of electrical and thermal stresses on the SG tape leads to rapid degradation of the groundwall insulation. The main objective of this paper is to introduce an effective (SG) system for these motors. The results of laboratory tests and computer simulations for a capacitive SG scheme based on conductive foils are presented and discussed. The results indicate that this method can effectively control both the electrical and thermal stresses at the endwinding region under both power frequency and repetitive fast rise time pulses from PWM drives.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.025
GPT teacher head0.246
Teacher spread0.221 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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