MétaCan
Menu
Back to cohort
Record W2993625464 · doi:10.1109/icems.2019.8922442

Slot–pole Selection for Concentrated Wound Consequent Pole PMSM with Reduced EMF and Inductance Harmonics

2019· article· en· W2993625464 on OpenAlexaff
Himavarsha Dhulipati, Shruthi Mukundan, Eshaan Ghosh, Ze Li, Budhika Guruwatta Vidalanage, Jimi Tjong, Narayan C. Kar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsHarmonicsCogging torqueInductanceStatorControl theory (sociology)Electromagnetic coilHarmonic analysisHarmonicTorqueMagnetWaveformComputer sciencePhysicsEngineeringVoltageAcousticsElectrical engineeringElectronic engineering

Abstract

fetched live from OpenAlex

Replacing half of the number of poles in a conventional surface permanent magnet synchronous machine (PMSM) reduces the use of rare earth materials delivering similar performance. However, in such a consequent pole configuration certain slot-pole numbers deliver high magnitudes of even order harmonics in the induced EMF waveforms leading to unbalanced magnetic force. Further, utilizing concentrated windings (CW) in the stator adds to this space harmonic content. In literature, slot-pole combinations were selected based on fundamental winding factor, cogging torque and net force on the machine, neglecting space harmonics content in inductance and induced EMF waveforms. Motivated by the drawbacks in the existing methods, in this paper, a novel inductance harmonics factor and EMF harmonics factor have been modelled using winding function method for a consequent pole multiphase CW PMSM. Furthermore, a gradient descent algorithm-based approach is implemented to optimally select slot-pole combination, with reduced inductance and EMF harmonics, for three-, five- and six-phase FSCW PMSM, with little prior knowledge about structural information of the machine. The inductance and induced EMF harmonics for optimal slot-pole combinations obtained from the algorithm are verified using finite element and experimental analysis.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.445

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicElectric Motor Design and AnalysisFrench-language works237,207