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Record W2343127142 · doi:10.1109/tmag.2016.2525805

A Novel Current Injection-Based Online Parameter Estimation Method for PMSMs Considering Magnetic Saturation

2016· article· en· W2343127142 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 Transactions on Magnetics · 2016
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsInductanceSaturation (graph theory)Control theory (sociology)Magnetic fluxPermanent magnet synchronous motorMagnetComputer scienceEstimation theoryElectromagnetic coilSynchronous motorMagnetic fieldPhysicsVoltageMathematicsAlgorithm

Abstract

fetched live from OpenAlex

This paper studies the online parameter estimation of permanent magnet synchronous motor (PMSM) with the consideration of magnetic saturation and proposes a novel current injection method to estimate parameters, including winding resistances, dq-axis inductances, and rotor flux. During the current injection, the inductances will vary due to magnetic saturation, neglecting which will cause great estimation error especially in the inductance estimation. This paper proposes to use simplified equations to model the self- and cross-saturation effects during the current injection. By incorporating this saturation model into the PMSM steady-state equations, the varying dq-axis inductances due to magnetic saturation as well as the rotor flux can be accurately estimated. In addition, the estimation of winding resistance is independent of other parameters and does not get affected by the magnetic saturation. The proposed approach is validated through both the numerical and experimental studies on a laboratory interior PMSM.

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 score0.640
Threshold uncertainty score0.937

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 head0.023
GPT teacher head0.269
Teacher spread0.246 · 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