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Record W2498038163 · doi:10.1109/itec.2016.7520215

MTPA fitting and torque estimation technique based on a new flux-linkage model for interior permanent magnet synchronous machines

2016· article· en· W2498038163 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsMcMaster University
FundersCanada Research Chairs
KeywordsFlux linkageTorqueControl theory (sociology)AmpereLinkage (software)MagnetDirect torque controlNonlinear systemFlux (metallurgy)Synchronous motorFinite element methodComputer scienceMathematicsEngineeringPhysicsControl (management)Current (fluid)Artificial intelligenceVoltageStructural engineeringMechanical engineeringInduction motorMaterials science

Abstract

fetched live from OpenAlex

Due to the nonlinearity of the flux-linkage profiles of the interior permanent magnet synchronous machine (IPMSM), the conventional motor model cannot be used for both maximum torque per ampere (MTPA) control and torque estimation. This paper proposes a nonlinear flux-linkage model for IPMSM with eight coefficients to fit the real d-axis flux-linkage, q-axis flux-linkage, MTPA, and torque. The corresponding torque equation and MTPA condition are presented. The factors in the proposed model can be obtained by solving an optimization problem with the limited information from the machine instead of the measurement throughout the map. The comparison of the characteristics between the proposed algorithm and FEA data is illustrated.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score0.584

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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

Quick stats

Citations4
Published2016
Admission routes2
Has abstractyes

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