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Record W3128644654 · doi:10.1109/tec.2021.3056557

Torque Ripple Reduction Method for Permanent Magnet Synchronous Machine Drives With Novel Harmonic Current Control

2021· article· en· W3128644654 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 Energy Conversion · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsControl theory (sociology)Torque rippleHarmonicDirect torque controlTorqueRotor (electric)Controller (irrigation)RippleHarmonic analysisStall torqueStatorDamping torqueComputer scienceEngineeringPhysicsElectronic engineeringVoltageInduction motorAcousticsElectrical engineering

Abstract

fetched live from OpenAlex

Torque ripple reduction has become an active research area for permanent magnet synchronous machine (PMSM) drives. This paper presents a novel torque ripple reduction method based on harmonic current control. In the proposed method, the optimal harmonic current solution based on the torque ripple model with minimum stator resistive loss is derived in the rotor reference frame (RRF). The optimal harmonic currents are injected by the proposed harmonic current controller (HCC), wherein the magnitude of harmonic currents are defined as controller variables. To estimate the harmonic currents, the proposed method also utilizes the least mean square (LMS) based adaptive filter (AF), wherein the coefficients are defined as feedback to the proposed HCC. The proposed methodology is demonstrated using both simulations and experiments and is verified to reduce torque ripples of PMSM drives over a wide range of speeds.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.818

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 head0.214
Teacher spread0.207 · 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