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Record W2913193341 · doi:10.1049/el.2018.8241

Direct torque and flux control of switched reluctance motor with enhanced torque per ampere ratio and torque ripple reduction

2019· article· en· W2913193341 on OpenAlex
Krishna Reddy Pittam, Deepak Ronanki, P. Parthiban

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

VenueElectronics Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSwitched reluctance motorDirect torque controlTorque rippleStall torqueTorque limiterControl theory (sociology)Torque motorTorqueReluctance motorDamping torqueAmpereReduction (mathematics)Materials sciencePhysicsComputer scienceEngineeringMathematicsElectrical engineeringControl (management)Induction motorVoltage

Abstract

fetched live from OpenAlex

A smooth torque control of switched reluctance motor (SRM) is essential to avoid speed fluctuations causing stability problems in vehicular applications. This can be accomplished by an appropriate motor design and/or use of direct control of torque in SRM. It is reported that high RMS current is required to minimise the torque ripple in the conventional direct torque and flux control (DTFC), thereby reducing the torque per ampere ratio. To overcome this issue, a new DTFC technique with improved torque per ampere ratio while minimising torque ripple in an SRM traction drive is presented. Results demonstrated that the proposed DTFC technique reduces torque ripple with enhanced torque per ampere. Finally, the performance of the proposed scheme is compared with conventional DTFC of a four‐phase (8/6) SRM to show the improvement in the traction drive.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.885

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 head0.002
GPT teacher head0.161
Teacher spread0.159 · 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