MétaCan
Menu
Back to cohort
Record W3172227273 · doi:10.1109/tia.2021.3086052

Magnetic Model Identification of Wound Rotor Synchronous Machine Using a Novel Flux Estimator

2021· article· en· W3172227273 on OpenAlexaff
Peyman Haghgooei, Ehsan Jamshidpour, Noureddine Takorabet, Davood Arab Khaburi, Babak Nahid‐Mobarakeh

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStatorFlux linkageRotor (electric)Flux (metallurgy)EstimatorMagnetic fluxControl theory (sociology)ExcitationSynchronous motorIdentification (biology)Frame (networking)Computer scienceEngineeringPhysicsMagnetic fieldMathematicsDirect torque controlMechanical engineeringArtificial intelligenceInduction motorElectrical engineeringMaterials science

Abstract

fetched live from OpenAlex

In this article, an online method is proposed to find the magnetic model of a wound rotor synchronous machine (WRSM). To identify the magnetic model, the stator flux is first estimated by considering two auxiliary variables and using a partially known model of the WRSM. Using this model, the stator flux is estimated at the dq reference frame. Then, to identify the magnetic model or in other words to find the relationship between the machine flux linkage and the machine currents, the stator flux is mapped in terms of stator and rotor excitation currents. To validate the proposed method, simulations and experimental tests are carried out on a WRSM.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.247
Teacher spread0.227 · 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 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

Citations10
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Industry ApplicationsSame topicElectric Motor Design and AnalysisFrench-language works237,207