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Record W2754802977 · doi:10.1109/tpel.2017.2754462

Common-Mode Voltage Reduction for Parallel CSC-Fed Motor Drives With Multilevel Modulation

2017· article· en· W2754802977 on OpenAlexafffund
Li Ding, Zhongyi Quan, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2017
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates - Technology Futures
KeywordsCommon-mode signalSeries and parallel circuitsInductorSpace vector modulationElectronic engineeringMotor driveVoltageConvertersModulation (music)Computer scienceInterleavingPower (physics)Control theory (sociology)EngineeringTopology (electrical circuits)InverterElectrical engineeringPhysicsAcousticsDigital signal processing

Abstract

fetched live from OpenAlex

In medium voltage high power (>1 MW) application, the parallel-connected current source converters (CSCs) are normally applied to increase the power region. By interleaving the paralleled CSCs, multilevel (five level) output current can be generated, and thus improving the harmonic performance. However, the common-mode voltage (CMV), common-mode current (CMC), and circulating current in the parallel CSC-fed motor drive system pose serious challenges to the system's operation, which should be addressed carefully. Moreover, for transformerless drives, the common-mode resonance under varying motor speeds is a potential problem due to the series connection of common-mode inductor and filter capacitor in the common-mode loop. This paper thoroughly investigates the common-mode resonance in transformerless parallel CSC system. A multilevel space-vector-modulation based method for the parallel CSC system is proposed to suppress the CMV and common-mode resonance. As a result, the circulating current and CMC excited by the CMV can be reduced correspondingly. The effectiveness of the proposed method is verified on a transformerless parallel-connected CSC 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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.

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

Citations31
Published2017
Admission routes2
Has abstractyes

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