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Record W4226181494 · doi:10.1109/tpel.2022.3159483

Maximum Torque Operation of Open-Winding Induction Motor Dual Drives Using a Floating Capacitor Bridge in the Field Weakening Region

2022· article· en· W4226181494 on OpenAlexafffund
Saeed Wdaan, Chatumal Perera, John Salmon

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsControl theory (sociology)Controller (irrigation)StatorEngineeringAC powerInduction motorInverterVector controlTransient (computer programming)CapacitorTorqueVoltageComputer scienceElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

A field weakening controller is presented that extends the speed range of a dual inverter drive using a floating capacitor bridge. The controller utilizes the stator current reference frame to decouple the motor demand into active and reactive components. This is used so that the main bridge supplies the real power and the floating bridge supplies the reactive power until the floating bridge reaches its maximum voltage. The main bridge is then used to supply some of the reactive demand, which in turn adds additional voltage boost that is used to extend the drive’s constant power region, improve the motor speed acceleration performance, output power, and torque. It is found that the presented controller can increase the drive speed extension ratio to 9.2 times the base speed compared to 5 when always operating the main bridge at unity power factor. The motor maximum fundamental voltage is 1.82 p.u when compared to that of a single inverter drive. This controller performance is demonstrated experimentally using both transient and steady-state analysis. The controller is also compared with other two field weakening controllers using the same regulator tunings and experimental setup.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.254
Teacher spread0.232 · 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 designSimulation or modeling
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

Citations13
Published2022
Admission routes2
Has abstractyes

Explore more

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