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Record W2017259399 · doi:10.1109/apec.2013.6520239

A reduced common-mode voltage space vector modulation method for current source converters

2013· article· en· W2017259399 on OpenAlexaff
Jian Shang, Yunwei Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSpace vector modulationCommon-mode signalControl theory (sociology)Voltage sourceSupport vector machineStatorConvertersModulation indexComputer scienceElectromagnetic coilModulation (music)VoltageHarmonicHarmonic analysisElectronic engineeringPulse-width modulationEngineeringPhysicsArtificial intelligenceElectrical engineeringAcoustics

Abstract

fetched live from OpenAlex

The common-mode voltage (CMV) produced from a converter system is a source of many problems. E.g. in the motor drive system, CMV might appear at the neutral point of the motor stator windings with respect to the ground and induce destructive bearing current. Reduced common-mode voltage space vector modulation (RCMV SVM) methods have been proposed in both voltage source converter (VSC) and current source converter (CSC) systems. The available RCMV SVMs reduce the CMV by avoiding the use of zero-state vectors. However, this will lead to some negative effects, such as shrink of modulation index range, increase of switching frequencies, and power quality performance deterioration, etc. In this paper, a RCMV SVM method for CSCs is proposed. It has almost the same harmonic performance compared to the conventional SVMs by allowing the use of zero-state vectors, but with much lower CMV. The effectiveness of the proposed RCMV SVM for CSCs is verified in an experimental prototype.

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: none
Teacher disagreement score0.973
Threshold uncertainty score0.863

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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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