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Record W2342831985 · doi:10.1109/tpel.2016.2529959

A Three-Level Space Vector Modulation Scheme for Paralleled Converters to Reduce Circulating Current and Common-Mode Voltage

2016· article· en· W2342831985 on OpenAlexafffund
Zhongyi Quan, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2016
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConvertersInductorSpace vector modulationCommon-mode signalModulation (music)Electronic engineeringVoltagePower (physics)Control theory (sociology)Voltage sourceComputer scienceTopology (electrical circuits)EngineeringPulse-width modulationElectrical engineeringPhysicsDigital signal processing

Abstract

fetched live from OpenAlex

For high-power applications, paralleling converters is a popular approach to increase the power capacity of the system. Circulating current has been a major concern for the implementation of paralleled converters. This paper proposes a three-level space vector modulation (SVM) scheme for a system with two paralleled voltage-source converters (VSCs) with common-mode inductor (CMI) or single-phase inductors. The proposed scheme aims to reduce the zero-sequence circulating current (ZSCC) and the magnitude of common-mode voltage (CMV) of the system simultaneously. The ZSCC patterns with respect to modulation schemes are first analyzed to provide a clear understanding of the generation of ZSCC. Based on the analysis, the proposed three-level modulation scheme is introduced. Furthermore, performance regarding the ZSCC peak value, impact on the common-mode current (CMC), CMI scaling analysis, and switching losses are analyzed and compared with the existing methods. The proposed method has been verified in 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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.264
Teacher spread0.239 · 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 designBench or experimental
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

Citations120
Published2016
Admission routes2
Has abstractyes

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