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Record W2610882876 · doi:10.1109/pedes.2016.7914291

Vector controlled drive to measure inductances of variable flux machine

2016· article· en· W2610882876 on OpenAlexaff
Rajendra Thike, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsMagnetizationInductanceControl theory (sociology)MagnetVector controlMagnetic fluxTorqueDirect torque controlMagnetic circuitPhysicsComputer scienceEngineeringElectrical engineeringInduction motorMagnetic fieldVoltage

Abstract

fetched live from OpenAlex

Variable flux machines use low coercive-force magnets to control the air-gap flux density. The magnet flux is controlled by applying direct-axis armature current pulses. The magnetization state of the magnets depends on the peak value of the direct-axis current. Any overshoot in the direct-axis current adversely affects the magnetization state. As the controller parameters are dependent on the inductance values, accurate estimation of inductances and their dependence on the magnetization level will allow a proper design of controllers, improving the dynamic performance of the drive. Further, the estimates of current dependent inductances can be used for utilization of the available reluctance torque. This paper presents an inductance measurement method for variable flux machines using vector controlled drive. The advantage of the proposed scheme is that the measurement of both direct and quadrature-axis inductances can be performed at any arbitrary rotor position, requiring no change in rotor setup either to demagnetize or re-magnetize the magnets. Experimental results are provided for various magnetization levels. The comparison of inductances show that both direct and quadrature-axis inductances decrease with an increase in the magnetization level.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score1.000

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)0.0010.000

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 head0.189
Teacher spread0.182 · 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.

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
Published2016
Admission routes1
Has abstractyes

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