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Record W2962848323 · doi:10.1109/access.2019.2930895

Mutually Coupled Switched Reluctance Motor: Fundamentals, Control, Modeling, State of the Art Review and Future Trends

2019· article· en· W2962848323 on OpenAlexafffund
Peter Azer, Berker Bilgin, Ali Emadi

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsSwitched reluctance motorControl theory (sociology)InductanceComputer scienceRotor (electric)Reluctance motorTorqueWaveformInverterMachine controlExcitationDirect torque controlVoltageControl engineeringControl (management)PhysicsInduction motorEngineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Switched reluctance motor (SRM) is gaining more interest in the last decades due to its simple and robust structure. SRMs are classified into conventional SRMs (CSRMs) and mutually coupled SRMs (MCSRMs). CSRMs are based on single-phase excitation and torque is generated by the variation of self-inductance with rotor position. MCSRMs are based on multi-phase excitation and torque is produced by the rate of change of both self- and mutual inductances. MCSRM has the advantages of using the standard voltage source inverter at balanced current operation, when the sum of the phase currents is zero, while CSRM requires an asymmetrical converter. This paper presents the state-of-the-art review of MCSRMs, including operating concept, winding, and pole configurations, control methods by using different current waveforms, performance comparison of MCSRM configurations, modeling methods, and future work for improving MCSRM performance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.234
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations55
Published2019
Admission routes2
Has abstractyes

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