MétaCan
Menu
Back to cohort
Record W2106914703 · doi:10.1109/imtc.1998.676820

Iron core losses: measurement and impact in induction motor vector control

2002· article· en· W2106914703 on OpenAlexaff
M. Benhaddadi, O. Touhami, Samir Moulahoum, G. Olivier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsStatorControl theory (sociology)Vector controlInduction motorTorqueRotor (electric)Core (optical fiber)Compensation (psychology)Direct torque controlFlux (metallurgy)Pulse-width modulationMagnetic coreVoltageController (irrigation)Machine controlEngineeringComputer scienceMaterials sciencePhysicsControl (management)Control engineeringElectromagnetic coilElectrical engineering

Abstract

fetched live from OpenAlex

In the present paper, the influence of iron core losses on the vector controlled induction motor performance is-presented. The equivalent iron core loss resistance is experimentally identified from no-load tests with PWM and sinusoidal supplies. The amount of detuning caused by the iron core losses is analysed for indirect rotor flux oriented control (RFOC) and stator flux oriented control (SFOC) of a voltage-fed induction machine. Based on simulation results, it is shown that the discrepancies between actual and imposed rotor flux, torque and errors in orientation angle are relatively important and must be compensated particularly for high performance applications. This is obtained by using a modified controller. The compensation adds to the system complexity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.028
GPT teacher head0.213
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicElectric Motor Design and AnalysisFrench-language works237,207