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Record W2344266133 · doi:10.1109/tmag.2016.2522100

Analytical Investigation Into Magnet Eddy Current Losses in Interior Permanent Magnet Motor Using Modified Winding Function Theory Accounting for Pulsewidth Modulation Harmonics

2016· article· en· W2344266133 on OpenAlexaff
Aiswarya Balamurali, Chunyan Lai, Aida Mollaeian, Voiko Loukanov, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Magnetics · 2016
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsHarmonicsMagnetStatorPulse-width modulationMagnetomotive forceEddy currentRotor (electric)PhysicsControl theory (sociology)Permanent magnet synchronous generatorElectromagnetic coilVoltageComputer scienceMechanics

Abstract

fetched live from OpenAlex

The study of the interaction between an interior permanent magnet (IPM) motor and a pulsewidth modulated (PWM) converter is increasingly becoming important, especially in the case of high-speed drives owing to the losses caused by modulation. Considering the significance of machine-converter interaction, this paper proposes a novel method to calculate magnet eddy current losses in an IPM used in traction applications by incorporating both PWM harmonics as well as rotor geometry and design parameters. The voltage harmonics from the PWM are incorporated into a series of steps in order to calculate the stator magnetomotive force (MMF) using the modified winding function theory. The rotor MMF and, subsequently, the magnet losses are computed using a magnetic circuit model incorporating the harmonics from the stator MMF as well as the flux barrier and the rotor geometry. The results have been demonstrated analytically for various cases of modulation and PWM input parameters to study the dependence of magnet losses on converter properties. The analytical results have been validated by finite-element analysis and experimental investigations.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.761
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.028
GPT teacher head0.251
Teacher spread0.223 · 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.

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

Citations28
Published2016
Admission routes1
Has abstractyes

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