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Record W2113640554 · doi:10.1109/pes.2007.385819

Starting Performance of Saturated Induction Motors

2007· article· en· W2113640554 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Power Engineering Society General Meeting · 2007
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsInduction motorSaturation (graph theory)Magnetic flux leakageStatorControl theory (sociology)Leakage (economics)Magnetic fluxLeakage inductanceFlux (metallurgy)Computer scienceEngineeringMaterials scienceInductanceMagnetPhysicsMagnetic fieldMechanical engineeringMathematicsVoltageElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The determination of the induction machine starting performance has traditionally been based on the constant parameter models. Magnetic saturation is unavoidable in most electrical machines and induction motors are no exception. So, an accurate study of their performance should necessarily consider the magnetic saturation effects. In this paper, a simple experimental procedures to determine the machine's main flux saturation characteristic and both stator and rotor leakage flux saturation characteristics are adopted. Three models of saturated induction motors are developed to predict the starting performance of a laboratory wound-rotor induction motor. The results calculated by the proposed models considering and ignoring the main and the leakage flux saturation are compared with the experimental results. The model that considers saturation both in the main and the leakage flux paths produces the most accurate starting responses.

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.

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)0.001
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 score0.331
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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