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Record W2143981997 · doi:10.1109/tia.2004.841018

The Effect of Surge Testing on the Voltage Endurance Life of Stator Coils

2005· article· en· W2143981997 on OpenAlexaff
J.H. Dymond, Meredith K. W. Stranges, N. Stranges

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2005
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsGeneral Electric (Canada)
Fundersnot available
KeywordsStatorSurgeElectromagnetic coilTransient (computer programming)VoltageElectrical engineeringOvervoltageInsulation systemEngineeringDielectric strengthMaterials scienceAutomotive engineeringStructural engineeringComputer science

Abstract

fetched live from OpenAlex

Surge tests with short rise times are used to ensure turn insulation integrity in form-wound vacuum-pressure impregnated (VPI) coils prior to resin treatment. Use of this test migrated from the evaluation of fully processed generator bars and random wound stators. The dielectric properties of the insulation in these systems are fully developed at the time of testing. This is not true of "green" (unimpregnated) VPI coils. Although surge testing is a valuable manufacturing quality test, an excessive transient electric field in uncured coils may cause irreversible changes to the insulation. These changes may affect the life of the insulation system after complete impregnation and cure. This paper describes laboratory tests and finite element simulations that examine the effect of applied surge test voltage and number of applied pulses on voltage endurance (VE) life. The sympathetic voltage response in a single coil as a function of its location within a winding is described for a stator undergoing green surge testing as part of a quality assurance program. Results of VE testing and sample dissection data are presented. Test voltage was identified a more significant factor on VE life than the number of pulses applied.

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)0.001
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 score0.132
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.259
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

Citations13
Published2005
Admission routes1
Has abstractyes

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