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Record W2945812707 · doi:10.1109/tec.2019.2914930

Exploring the Phase Angle of Measured Speed Harmonic for Efficient Permanent Magnet Temperature Estimation of PMSMs

2019· article· en· W2945812707 on OpenAlexaff
Guodong Feng, Chunyan Lai, Jimi Tjong, Narayan C. Kar

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2019
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia UniversityUniversity of Windsor
Fundersnot available
KeywordsControl theory (sociology)HarmonicsInductanceMagnetPhase angle (astronomy)HarmonicHarmonic analysisPhase (matter)Three-phaseTangentElectromagnetic coilSynchronous motorPhysicsComputer scienceEngineeringVoltageElectronic engineeringAcousticsMechanical engineeringMathematicsElectrical engineeringOpticsGeometry

Abstract

fetched live from OpenAlex

This paper investigates the use of the phase angle of the measured speed harmonics for efficient permanent magnet (PM) temperature estimation of permanent magnet synchronous machine (PMSM). Based on the mechanical model, a linear estimation model is derived to model the relationship between the PM temperature and the phase angle of the speed harmonic. This model shows that there is a linear relationship between the PM temperature and the tangent of the phase angle. Therefore, the PM temperature can be directly estimated from the phase angle of the speed harmonic using the derived model. Moreover, the derived estimation model does not involve machine parameters such as inductance and winding resistance, thus the proposed estimation approach is independent from machine parameter variation. The proposed approach is evaluated on a laboratory interior PMSM.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.364

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 head0.023
GPT teacher head0.213
Teacher spread0.190 · 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 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

Citations12
Published2019
Admission routes1
Has abstractyes

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