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Investigations on Winding Modeling Techniques for Electrical Stress Analysis in VSI-Fed Traction Electric Machines

2019· article· en· W3022221624 on OpenAlexaff
Ashutosh Patel, Chunyan Lai, K. Lakshmi Varaha Iyer, Gerd Schlager, Narayan C. Kar

Bibliographic record

Venue2019 IEEE Transportation Electrification Conference (ITEC-India) · 2019
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsElectromagnetic coilTraction (geology)Voltage source inverterStress (linguistics)VoltageTraction motorComputer scienceEngineeringInverterElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Traction motors are exposed to high dv/dt pulses when driven by a voltage source inverter (VSI). Due to this type of voltage excitation, machine's winding insulation system experiences additional dielectric stress, which can result in premature insulation failure in the machine windings. To analyze this electrical stress, various modeling techniques have been presented to model the machine windings, which can predict the voltage and current distributions among coils and turns of the windings. In this paper, different modeling techniques are investigated. Comparative analysis and results from case studies of the selected modeling techniques are presented.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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 designSimulation or modeling
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

Citations0
Published2019
Admission routes1
Has abstractyes

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