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Challenges in the Control of Brushless Doubly Fed Reluctance Machines

2025· article· en· W4412129337 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInduction Heating and Inverter Technology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMagnetic reluctanceReluctance motorControl theory (sociology)Switched reluctance motorControl (management)Computer scienceAutomotive engineeringControl engineeringTorqueElectrical engineeringEngineeringPhysicsArtificial intelligenceMagnetThermodynamics

Abstract

fetched live from OpenAlex

This paper introduces some of the challenges encountered when operating Brushless Doubly Fed Reluctance Machines (BDFRM) in fully controlled systems. BDFRMs operate by modulating the magnetomotive forces (MMFs) from two stator windings with different pole numbers through a rotor which has a reluctance structure with number of poles that is again different to both stator windings. Previous work on these machines focuses on the desirable harmonics in the machine, which produce the required torque. However, the rotor structure also produces unwanted flux harmonics, which result in either unwanted torque harmonics or induced damping currents. This paper investigates these harmonics and the challenges imposed by them. The relationship between the competing needs to control currents to their desired fundamental values, and to control speed, which requires minimal torque ripple in low inertia systems is also investigated. A second order low-pass filter is implemented as a solution to attenuate the harmonics. Experimental results are provided to demonstrate the effectiveness and impact of the proposed approach.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.142

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 head0.023
GPT teacher head0.243
Teacher spread0.220 · 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