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Record W1986040835 · doi:10.1109/iecon.2013.6699638

Transient distribution of voltages in induction machine stator windings resulting from switching of power electronics

2013· article· en· W1986040835 on OpenAlexaff
Peter Nußbaumer, C. Zoeller, Thomas M. Wolbank, Markus Vogelsberger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsOvervoltageTransient (computer programming)StatorElectromagnetic coilAdjustable-speed driveReliability (semiconductor)InverterVoltageFault (geology)Power electronicsPower (physics)Electrical engineeringEngineeringTransient voltage suppressorComputer scienceElectronic engineeringControl theory (sociology)Physics

Abstract

fetched live from OpenAlex

The fields of application of adjustable speed drives fed by voltage source inverters is constantly increasing. For better exploitation of the system's capabilities all drive components are more and more operated near and even above their rated values. This leads to more strains for the drive. However, at the same time the demands for reliability are also increasing. To reach this requirement different strategies like fault tolerant design, fault detection and condition monitoring can be implemented. However, to implement such strategies a deep understanding of the effects that lead to problems or faults in the drive system is necessary. The fast switching of modern voltage source inverters leads to transient overvoltage stressing the machine's insulation system. A short literature study presenting the characteristic of and the parameters influencing the non-linear voltage distribution in the stator winding of inverter-fed machines will be presented. Furthermore the findings will be compared with experimental results on an induction machine with tapped windings.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.353

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 head0.004
GPT teacher head0.186
Teacher spread0.182 · 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

Citations25
Published2013
Admission routes1
Has abstractyes

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