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Record W2046253245 · doi:10.1109/elinsl.2008.4570300

Evaluation of Grading System of Large Motors AC Stator Windings

2008· article· en· W2046253245 on OpenAlexaff
Ramtin Omranipour, Saeed Ul Haq

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsGeneral Electric (Canada)
Fundersnot available
KeywordsMaterials scienceComposite materialStatorElectromagnetic coilSilicon carbideVoltageEpoxyHigh voltageGrading (engineering)VarnishInsulation systemElectrical conductorElectrical engineeringCoatingEngineering

Abstract

fetched live from OpenAlex

In this paper, the field dependent characteristics of different stress grading tapes used for high voltage form-wound coils have been examined. High voltage insulation systems require voltage stress grading to suppress the potential gradient and to ensure proper operation. Without these materials, the electric fields in the endarm region will lead to discharge in the surrounding air with potential degradation of the insulation system. The grading materials used in this investigation were woven polyester fabric tape loaded with semi-conductive varnish that included silicon carbide. In order to determine if the vacuum pressure impregnation (VPI) epoxy resin has an influence on grading performance, surface potential within the stress grading regions of sample coils was measured at test voltages of 8 kV and 14 kV. The measurements were performed prior to VPI (at green stage), after pre-bake at 160degC, and post VPI. The influence of high resistive surface contaminants on the grading was also examined. The experimental results reveal that introduction of the VPI resin can influence the electrical characteristics of the grading systems. In addition, a high resistivity contaminant can influence the performance of the grading system.

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)0.002
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 score0.029
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.039
GPT teacher head0.284
Teacher spread0.246 · 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

Citations12
Published2008
Admission routes1
Has abstractyes

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