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Record W2115354162 · doi:10.1109/eic.2009.5166342

Partial discharge testing of random wound stators during short risetime voltage surges

2009· article· en· W2115354162 on OpenAlexaff
G.C. Stone, Ian Culbert

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsOntario Power Generation
Fundersnot available
KeywordsPartial dischargeSurgeVoltageElectromagnetic coilStatorElectrical engineeringDetectorInsulation systemInverterHigh voltageEngineeringMaterials science

Abstract

fetched live from OpenAlex

Modern inverter fed motors often produce short risetime, high magnitude voltage surges that may lead to partial discharge in the random wound stator windings. Detection of PD during such voltage surges can not be done with the usual PD detectors described by ASTM D1868. Instead a detector that can separate PD pulse currents from a short risetime voltage surge is required. This paper describes a PD detector that can measure PD pulses when the applied voltage is a voltage surge with a risetime as short as 100 ns. PD data has been collected from dozens of stators, many of which had known insulation problems. In most cases the PDIV easily met the requirements of a new IEC technical specification that sets the partial discharge inception voltages for random wound stators. Since most of these stators had obvious insulation system deficiencies, it seems that the PDIV levels set by IEC TS 60034-18-41 may be too low.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0010.000

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 head0.018
GPT teacher head0.250
Teacher spread0.232 · 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.

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

Citations31
Published2009
Admission routes1
Has abstractyes

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