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Field Weakening Operation of Open-Winding Induction Motor Dual Drives Using a Floating Capacitor Bridge Inverter

2021· article· en· W3215141433 on OpenAlexafffund
Saeed Wdaan, Chatumal Perera, John Salmon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsInverterStatorPower factorControl theory (sociology)Induction motorTransient (computer programming)VoltageAC powerPower (physics)CapacitorElectrical engineeringEngineeringGrid-tie inverterComputer sciencePhysicsMaximum power point trackingControl (management)

Abstract

fetched live from OpenAlex

A field weakening control scheme of open-ended winding induction motors with a floating capacitor bridge is presented. The main bridge is operated at unity power factor to maximize the output mechanical power until the floating bridge hits its maximum voltage. After which, the main bridge is operated to supply some reactive demand to extend the drive speed extension ratio to 9.2 times the base speed compared to 5 for only unity power factor operation. The maximum voltage this scheme can supply is 0.83 p.u, 0.52 p.u, and 0.17 p.u higher compared to the single inverter drive, unity power factor, and single DC-link dual inverter drives, respectively, and only 0.17 p.u lower than that of two separate DC-links dual inverter drive. The presented scheme is also compared with four field weakening control schemes, three of which are dual inverter drive schemes, in terms of maximum stator voltage, speed extension ratio, and available output power. The feasibility of the control scheme is experimentally demonstrated by performing transient and steady state analysis.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.629

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 communication0.0000.001
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 head0.052
GPT teacher head0.272
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

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

Citations3
Published2021
Admission routes2
Has abstractyes

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