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Record W2923735614 · doi:10.1109/tpel.2019.2906557

Investigation of a Practical Convex-Optimization-Based Sensorless Scheme for IPMSM Drives

2019· article· en· W2923735614 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicSensorless Control of Electric Motors
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Excellence Research Chairs, Government of Canada
KeywordsScheme (mathematics)Control theory (sociology)Rotor (electric)Reduction (mathematics)Convex optimizationProcess (computing)TorqueVariable (mathematics)Optimization problemRegular polygonComputer scienceEngineeringMathematicsAlgorithmPhysicsControl (management)

Abstract

fetched live from OpenAlex

This paper presents a practical convex-optimization-based sensorless scheme for interior permanent magnet synchronous motor (IPMSM) drives. The proposed sensorless scheme simplifies the optimization process that employs only one variable as compared with two variables in the existing optimization-based methods. The reduction in variables substantially reduces the computational burden in the proposed scheme and, hence, is suitable for practical applications. Additionally, the proposed algorithm is able to estimate the rotor position regardless of the pattern of the high-frequency (HF) injected signal under low-speed conditions. This characteristic has been investigated in this paper where three typical HF injection patterns are compared. The performance of the proposed scheme is experimentally validated.

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 categoriesMeta-epidemiology (narrow)
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 score0.839
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
Bibliometrics0.0000.000
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 head0.225
Teacher spread0.215 · 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