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Record W3003478080 · doi:10.4271/2020-01-0469

Dynamic Modeling of an Interior Permanent Magnet Machine with Space-Vector-Modulation-Based Voltage Source Inverter

2020· article· en· W3003478080 on OpenAlexaff
Yihui Li, Brock Howey, Jianbin Liang, Ehab Sayed, Arfakhshand Ali Qazalbash, Michael Kasprzak, Dhafar Al-Ani, Ali Emadi, Berker Bilgin

Bibliographic record

VenueSAE International Journal of Advances and Current Practices in Mobility · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHarmonicsMagnetFinite element methodWaveformInverterDynamic simulationVoltageTorqueCoupling (piping)Control theory (sociology)Computer scienceModulation (music)EngineeringElectronic engineeringElectrical engineeringPhysicsMechanical engineeringAcousticsSimulationStructural engineering

Abstract

fetched live from OpenAlex

This paper presents a dynamic model for an interior permanent magnet (IPM) machine with a space-vector-modulation-based voltage source inverter. The dynamic model considers spatial harmonics, cross-coupling and magnetic saturation. In order to include the nonlinear electromagnetic characteristics of the IPM machine, the dynamic model is built based on the current-flux look-up tables obtained from finite element analysis (FEA). The model is co-simulated with the drive system, which considers the effects of the modulation technique and the switching frequency. The dynamic performance of a 60/8 IPM machine is analyzed using the dynamic model at different operating conditions and then validated with the torque waveforms obtained from FEA. The results show that dynamic performance can be analyzed accurately and more quickly using the dynamic model presented in this paper.

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)0.000
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.287
Teacher spread0.273 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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Same venueSAE International Journal of Advances and Current Practices in MobilitySame topicElectric Motor Design and AnalysisFrench-language works237,207