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Record W2124442624 · doi:10.1109/pesc.2005.1581990

Multilevel Current Source Inverters with Phase Shifted Trapezoidal PWM

2006· article· en· W2124442624 on OpenAlexaff
Dewei Xu, Bin Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTotal harmonic distortionPulse-width modulationInverterRectifier (neural networks)Control theory (sociology)Topology (electrical circuits)Induction motorWaveformThree-phaseVoltageCurrent sourceComputer scienceCurrent (fluid)Electronic engineeringMotor driveH bridgeEngineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

A 7-level current source inverter (CSI) is proposed for high-power applications. The inverter topology consists of three bridge current source inverters whose outputs are connected in parallel for a large induction motor. The multilevel inverter is fed by an 18-pulse SCR rectifier with three isolated outputs for the reduction of supply-side current THD and the elimination of possible circulating current among the inverters. A phase-shifted trapezoidal PWM (TPWM) scheme is proposed for the inverter to generate multiple current levels. Optimal phase angles are investigated for the reduction of inverter current THD and weighted THD (WTHD). Similar to multilevel voltage source inverters, the multilevel CSI can produce motor-friendly current and voltage waveforms with limited switching frequencies, which is a desirable feature for large motor drives

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.009
GPT teacher head0.207
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations55
Published2006
Admission routes1
Has abstractyes

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