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Record W2902341807 · doi:10.23919/icems.2018.8549176

Investigation of Phase Angle Displacements in Six-Phase PMSM with Concentrated Windings for Reduced MMF Harmonics

2018· article· en· W2902341807 on OpenAlexaff
Himavarsha Dhulipati, Shruthi Mukundan, Wenlong Li, Jimi Tjong, Narayan C. Kar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsHarmonicsElectromagnetic coilPhase (matter)Phase angle (astronomy)Three-phaseHarmonic analysisControl theory (sociology)Materials sciencePhysicsComputer scienceEngineeringElectrical engineeringOpticsElectronic engineeringVoltage

Abstract

fetched live from OpenAlex

Multiphase concentrated wound (CW) permanent magnet synchronous machines (PMSMs) have high content of space harmonics in the magneto-motive force (MMF) waveforms. These harmonics result in distorted voltage waveforms and cause additional losses thereby deteriorating the machine's performance. However, the magnitude of space harmonics in multi-phase machines is lesser when compared to three-phase CW PMSM. In order to eliminate and/or reduce space harmonics even further in the MMF waveforms, this paper investigates non-conventional phase angle displacements for a six-phase 36-slot/34-pole CW PMSM. The six-phase CW PMSM considered has two independent three-phase windings. Conventional six-phase CW PMSM have phase angle displacements of 60° (symmetrical configuration) or 30° (asymmetrical configuration) between the two-phase sets. Based on star of slots method, a number of possible phase shifts between the two sets of three phase windings are investigated for reduced MMF harmonics, improved demagnetization and other performance characteristics for the 36-slot/34-pole PMSM and are compared along with conventional phase displacement angles of 60° and 30°.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.023
GPT teacher head0.270
Teacher spread0.247 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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