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Record W2944224097 · doi:10.1109/pedes.2018.8707820

Experimental Investigation of MTPA Trajectory of Synchronous Reluctance Machine

2018· article· en· W2944224097 on OpenAlexaff
Rajendra Thike, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)TorqueStatorRotor (electric)TrajectoryMagnetic reluctanceAmpereDirect torque controlMathematicsCurrent (fluid)Computer scienceEngineeringPhysicsMagnetInduction motorMechanical engineeringVoltage

Abstract

fetched live from OpenAlex

The output torque of a Synchronous Reluctance Machine (SynRM) varies non-linearly with the stator current. It is represented as torque angle characteristics with the stator current as a parameter. To obtain a certain output torque, there is a large combination of stator current magnitudes and current angles. Thus, in order to minimize the inverter current and the stator copper loss, it is desirable to maintain the torque to current ratio at its maximum value. This paper evaluates four experimental methods to obtain the torque angle curves and Maximum Torque Per Ampere (MTPA) trajectory of SynRMs. Based on the experiments performed on a prototyped SynRM, adequacy of the static torque angle curves obtained for the locked rotor condition for MTPA is investigated. Experimental results suggest that the MTPA trajectory obtained by locked rotor test with varying both the rotor position and current vector produces a fairly accurate MTPA trajectory of the SynRM. When either rotor position or the current vector alone is varied to obtain the torque angle curve and the MTPA trajectory by locked rotor test, the obtained torque angle curves and MTPA trajectory deviate from the actual torque angle curves and the actual MTPA trajectory.

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 score0.064
Threshold uncertainty score0.310

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 head0.008
GPT teacher head0.207
Teacher spread0.198 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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