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Record W2748521540 · doi:10.1109/tpel.2017.2740782

Common-Mode Voltage Elimination for Variable-Speed Motor Drive Based on Flying-Capacitor Modular Multilevel Converter

2017· article· en· W2748521540 on OpenAlexaff
Sixing Du, Bin Wu, Navid R. Zargari

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2017
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPulse-width modulationModular designControl theory (sociology)VoltageCapacitorStatorMotor driveModulation (music)Common-mode signalElectromagnetic coilComputer scienceEngineeringElectronic engineeringPhysicsElectrical engineeringAcousticsDigital signal processing

Abstract

fetched live from OpenAlex

The flying-capacitor modular multilevel converter (FC-MMC) overcomes the low/zero-speed operation issues of conventional MMC-based drive. The FC-MMC injects no common-mode voltage (CMV) to the drive system. However, the pulse width modulator (PWM) introduces switching ripples, which appear as CMV on motor stator windings. To completely get rid of the CMV, an elimination method based on a new modulation scheme is proposed in this paper. The modulation algorithm places the switching pulses end to end for upper and lower arms, respectively. The consistent switching positions between upper and lower arms lead to the complete CMV elimination for FC-MMC-based drive system in entire speed range. Although the proposed method slightly increases the switching frequency as compared to conventional phase-shifted PWM, it is still acceptable in medium-voltage applications. The validation of the proposal is proved by simulations and experiments.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.247
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 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

Citations54
Published2017
Admission routes1
Has abstractyes

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