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Record W3023930503 · doi:10.1109/tec.2020.2992487

Speed Harmonic Based Decoupled Torque Ripple Minimization Control for Permanent Magnet Synchronous Machine With Minimized Loss

2020· article· en· W3023930503 on OpenAlexaff
Guodong Feng, Chunyan Lai, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of WindsorConcordia University
Fundersnot available
KeywordsControl theory (sociology)Torque rippleTorqueRippleHarmonicController (irrigation)Harmonic analysisMachine controlMinificationComputer scienceDirect torque controlEngineeringPhysicsControl engineeringVoltageControl (management)Electronic engineeringInduction motor

Abstract

fetched live from OpenAlex

This article proposes a closed-loop decoupled current control for torque ripple minimization (TRM) of permanent magnet synchronous machines (PMSMs) by using the speed measurements. In the proposed control, a decoupled scheme is developed to control the harmonic currents for TRM, in which the control of phase angle and magnitude is decoupled to simplify the controller design. The decoupled scheme consists of two PI controllers and one control rule: one PI is responsible for phase angle control, the other is responsible for magnitude control, and the control rule is responsible for coordinating the two PIs for TRM. The harmonic currents can produce additional loss, and thus this paper derives the optimal condition for TRM with minimized loss. With the derived condition, one can control q-axis current and calculate d-axis current from the derived condition, which can minimize the torque ripple with minimized loss and simplify the control structure. The proposed decoupled approach is evaluated with extensive experiments and comparative study on a laboratory PMSM drive.

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 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: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score0.987

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)0.0010.000

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 head0.007
GPT teacher head0.179
Teacher spread0.173 · 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 teacher head, 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

Citations29
Published2020
Admission routes1
Has abstractyes

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