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Record W3174620160 · doi:10.1109/tia.2021.3091664

Current Injection-Based Simultaneous Stator Winding and PM Temperature Estimation for Dual Three-Phase PMSMs

2021· article· en· W3174620160 on OpenAlex
Ze Li, Guodong Feng, Chunyan Lai, Wenlong Li, Narayan C. Kar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia UniversityUniversity of Windsor
Fundersnot available
KeywordsStatorInductanceControl theory (sociology)Lookup tableTorqueVoltageExtended Kalman filterMagnetKalman filterDual (grammatical number)EngineeringComputer scienceElectrical engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

This article proposes a current injection-based simultaneous stator winding and permanent magnet (PM) temperature tracking technique for dual three-phase permanent magnet synchronous machines (PMSMs). The estimation models involving winding and PM temperatures are derived for different control strategies with the cancelation of inductances. To improve the tracking performance, a Kalman filter is used for simultaneous winding and PM temperature estimation. Compared with the existing methods, the proposed simultaneous winding and PM temperature estimation technique is independent of the voltage-source nonlinearity effect and is applicable to various machine operating conditions. The main features of the proposed method are underscored by the cancelation of inductance and the elimination of the need for a lookup table. The test results on a laboratory dual three-phase PMSM are used to validate the proposed method under different speed and torque conditions.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.851

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 integrity0.0000.001
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 head0.014
GPT teacher head0.273
Teacher spread0.258 · 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