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Record W2081960443 · doi:10.1109/ecce.2010.5617992

A Square-wave Controller for a high speed induction motor drive using a three phase floating bridge inverter

2010· article· en· W2081960443 on OpenAlexafffund
Jeffrey Ewanchuk, 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
KeywordsInverterInduction motorVoltageControl theory (sociology)EngineeringThree-phaseElectromagnetic coilAC powerElectrical engineeringComputer science

Abstract

fetched live from OpenAlex

A square-wave voltage control scheme is presented for a high speed induction motor drive that uses a dual 3 phase inverter system and open ended motor windings. The drive main inverter bridge is connected to the dc battery source and a second inverter bridge is floating with no dc power source. The floating bridge dc voltage is allowed to naturally fluctuate and used to provide voltage boosting at high speeds, well above the maximum possible when using just the main bridge. This voltage boosting can also be used to compensate for fluctuations in the dc battery voltage. A second controller feature forces the main bridge to operate at a unity displacement power factor, lowering the main inverter losses and the rms current drawn from the battery - while maximizing the reactive support available to the machine. Significantly, these features are obtained inherently without having to switch between different inverter switching patterns or monitor the load current magnitude or phase. Experimental results are used to illustrate the principal of the natural reactive compensation of the floating bridge and to verify drive operation on a 2HP, 1800 rpm induction machine.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.993

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 head0.046
GPT teacher head0.259
Teacher spread0.213 · 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

Citations23
Published2010
Admission routes2
Has abstractyes

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