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Record W2100451645 · doi:10.1109/elinsl.2006.1665337

Study of Stress Grading Systems Working Under Fast Rise Time Pulses

2006· article· en· W2100451645 on OpenAlexafffund
Fermín P. Espino‐Cortés, Yuseph Montasser, Shesha Jayaram, E.A. Cherney

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaInstituto Politécnico Nacional
KeywordsWaveformGrading (engineering)Pulse-width modulationConvertersRise timeVoltageElectromagnetic coilMaterials scienceStress (linguistics)Electrical engineeringElectronic engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Stress grading coatings of cable and coil terminations are considerably affected by the high dV/dt present in PWM voltage source converters. As repetitive steep transients can result in the development of hot spots and enhanced electric fields in the stress grading (SG) system, premature failures of power apparatus can occur. Stress grading systems need to be improved to work under such conditions and to obtain the desired stress relief. In this work, a stress grading system that can help to control the electric stress as well as the hot spots under fast rise time pulses is studied. It is important to design and test these systems considering the real PWM waveform in which both the fast pulses and the fundamental low frequency are included. With that purpose a fast single-phase low power two-level PWM generator was used to evaluate the stress grading system. Also this generator was used to measure some of the dielectric properties of the material

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 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 score0.380
Threshold uncertainty score0.376

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)0.0000.000

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 head0.020
GPT teacher head0.242
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 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

Citations17
Published2006
Admission routes2
Has abstractyes

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