MétaCan
Menu
Back to cohort
Record W2885672232 · doi:10.1109/tte.2018.2865908

Braking a Variable Flux-Intensifying IPMSM in Minimal Time

2018· article· en· W2885672232 on OpenAlexafffund
Akrem Mohamed Aljehaimi, Pragasen Pillay

Bibliographic record

VenueIEEE Transactions on Transportation Electrification · 2018
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsConcordia University
FundersConcordia University
KeywordsControl theory (sociology)VoltageTorqueMaximizationDynamic brakingInverterComputer scienceMagnetAutomotive engineeringVariable (mathematics)RetarderPhysicsEngineeringElectrical engineeringMathematicsControl (management)

Abstract

fetched live from OpenAlex

This paper proposes a method for braking the variable flux-intensifying interior permanent-magnet synchronous motor (FI IPMSM) under a minimal time condition while operating in the high-speed region. The rare-earth PMs retain its magnetization as the flux weakening current is decreased. This inherent feature of the PMs makes the braking torque maximization possible for IPMSMs, thus minimal-time braking can be achieved. In contrast, the low-coercive magnets utilized in variable-flux IPMSMs are partially demagnetized in the highspeed region. This means that the variable-flux IPMSM takes a longer time to stop if compared to the IPMSM. Supplying a magnetizing current that ranges from 1 to 3 p.u. in the highspeed region while the inverter is running out of voltage is quite challenging. In this paper, an analytical solution to the amount of magnetizing current required to maximize the braking torque based on the available voltage to ensure minimal-time braking is presented. The simulated and experimental results obtained using a 5-hp variable FI IPMSM show the validity of the proposed braking scheme. A front-end active rectifier is utilized to recuperate the braking energy and maintain the dc bus voltage constant.

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: none
Teacher disagreement score0.885
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Transportation ElectrificationSame topicElectric Motor Design and AnalysisFrench-language works237,207