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Record W2616232498 · doi:10.1109/apec.2017.7930754

Common-mode resonance suppression for parallel CSC-fed high power medium voltage drives with multilevel modulation

2017· article· en· W2616232498 on OpenAlexaff
Li Ding, Zhongyi Quan, Yunwei Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCommon-mode signalTopology (electrical circuits)Space vector modulationInductorSeries and parallel circuitsComputer scienceCapacitorVoltagePower (physics)Modulation (music)Electronic engineeringConvertersResonance (particle physics)Control theory (sociology)Motor driveEngineeringPulse-width modulationPhysicsElectrical engineeringAcoustics

Abstract

fetched live from OpenAlex

In high power application, the parallel connected current source converters (CSC) are applied to increase the power region. This parallel connected CSC topology can lead to multilevel output current and improve the harmonic performance. However, the common-mode voltage (CMV), common-mode current (CMC) and circulating current in the parallel CSC-fed motor drive system can cause serious problems which should be taken into consideration. Moreover, the common-mode resonance is a potential problem due to the series connection of inductor and capacitor in the common-mode loop under adjustable motor speed. This paper investigates the causes of common-mode resonance in parallel CSC system compared with single CSC system thoroughly and a multilevel space-vector-modulation (SVM) based method applied in parallel CSC system is proposed to suppress the CMV and common-mode resonance. The effectiveness of the proposed method is verified on a transformerless parallel connected CSC-fed motor drive system by both simulation and experiment.

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 score0.790
Threshold uncertainty score0.754

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 head0.016
GPT teacher head0.250
Teacher spread0.235 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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