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Record W2775765383 · doi:10.1109/iecon.2017.8216332

High-resolution low-cost rotor position sensor for traction applications

2017· article· en· W2775765383 on OpenAlexaff
Lesedi Masisi, Pragasen Pillay

Bibliographic record

VenueIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsPosition sensorTotal harmonic distortionPosition (finance)Rotor (electric)ResolverHall effect sensorEncoderTorqueControl theory (sociology)Traction (geology)Automotive engineeringEngineeringComputer scienceMagnetElectrical engineeringVoltageMechanical engineeringControl (management)PhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The paper presents for the first time the use of a low cost Hall effect position sensor for a synchronous reluctance machine (SynRM). The use of vector control for a SynRM requires information of the rotor position. Already the SynRM is independent of the use of permanent magnets (PMs) which results in reduced cost of the SynRM. Hence the use of a low cost position sensor contributes to an additional cost reduction of the electric drive for traction applications. The paper also presents a comparison between the low cost position sensor with the digital hall effect position sensor used in the electric power steering (EPS) machines. The different dynamics of the SynRM were demonstrated through the use of the low cost position sensor. The low cost position sensor had a relatively simpler algorithm for position information and better position resolution. Through the use of the low cost position sensor the machine produced low torque ripples of less than 9% with a total current harmonic distortion (THD) of less than 2%. The proposed position sensor registered an error less than 1 mechanical degree when compared with a 12-bit industrial encoder.

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 score0.425
Threshold uncertainty score0.863

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.263
Teacher spread0.222 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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Same venueIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics SocietySame topicElectric Motor Design and AnalysisFrench-language works237,207