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Model-Free Predictive Pulse Pattern Control for Permanent Magnet Synchronous Motor Drives

2022· article· en· W4310449811 on OpenAlexaff
Dianxun Xiao, Battur Batkhishig, Aathira Karuvaril Vijayan, Alan Dorneles Callegaro, Rohit Baranwal, Ali Emadi

Bibliographic record

Venue2022 IEEE Energy Conversion Congress and Exposition (ECCE) · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTotal harmonic distortionStatorFlux linkagePulse-width modulationComputer scienceControl theory (sociology)Modulation indexDistortion (music)Control (management)Artificial intelligenceInduction motorElectrical engineeringDirect torque controlEngineeringVoltageAmplifierTelecommunications

Abstract

fetched live from OpenAlex

High-power permanent magnet synchronous motor (PMSM) drives require an efficient control strategy. Model predictive pulse pattern control (MP3C) is a promising solution in reducing total harmonic distortion (THD) when the machine operates at a high modulation index. Existing research on MP3C for PMSMs regulates phase currents indirectly by tracking the reference stator flux linkage from a machine model. However, the method would result in static current errors when the motor parameter mismatch exists. This article proposes a model-free predictive pulse pattern control (MFP3C) with current tracking capability to solve the problem. The new approach can offer the same THD level as the MP3C and eliminate the parameter dependence issue simultaneously. A comparative study on a simulated PMSM model is conducted to demonstrate the advantages of the proposed design.

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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.187
Teacher spread0.180 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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