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Record W2079938058 · doi:10.1109/apec.2013.6520200

PWM control of a dual inverter drive using an open-ended winding induction motor

2013· article· en· W2079938058 on OpenAlexaff
R. Ul Haque, Alex Kowal, Jeffrey Ewanchuk, John Salmon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsUniversal motorInduction motorMotor drivePulse-width modulationThyristor driveControl theory (sociology)AC motorVoltageCapacitorEngineeringInverterDC motorComputer scienceElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

A dual pwm inverter drive operating an open-winding induction motor can be an attractive motor-drive system when operated from a dc battery source. Operating one of the inverters using a floating dc-link capacitor provides two main functions: reactive voltage support for the main bridge connected to the dc battery voltage source; voltage boosting to increase the motor terminal voltage. The latter feature eliminates the requirement to use a separate dc-dc converter to provide a voltage boost. The resultant drive system can provide several benefits such as: lowering the average battery current by operating the main bridge at unity power factor; improving the motor torque and efficiency over a wide operating speed range; compensation for battery voltage fluctuations; lowering the motor harmonic losses; elimination of common-mode circulating currents. This paper describes a pwm control scheme for the two inverters to regulate the floating bridge capacitor voltage over the entire dynamic speed range of the motor, whilst also providing the desired motor demand voltage. Experimental results are used to verify both the operation of the motor-drive system and the regulation of the floating capacitor voltage. Experimental results using an induction motor load confirm the practical feasibility of the motor-drive system.

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 score0.977
Threshold uncertainty score0.829

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)0.0010.000

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 head0.035
GPT teacher head0.240
Teacher spread0.206 · 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

Citations46
Published2013
Admission routes1
Has abstractyes

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