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Record W3042755996 · doi:10.1109/tia.2020.3009078

A Novel 2$N$ + 1 Carrier-Based Pulse Width Modulation Scheme for Modular Multilevel Converters With Reduced Control Complexity

2020· article· en· W3042755996 on OpenAlexafffund
Deepak Ronanki, Sheldon S. Williamson

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2020
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModular designPulse-width modulationConvertersElectronic engineeringWaveformModulation (music)Computer scienceVoltageEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This article presents a simple pulse width modulator to achieve both N + 1 and 2N + 1 modulation in a modular multilevel converter. The proposed implementation needs a single triangular waveform to control a modular multilevel converter with a large number of submodules. Hence, the proposed approach significantly reduces the required triangular carrier count and solves the practical implementation issues associated with the carrier-based pulse width modulation schemes in the digital control platforms. The performance of the proposed approach is validated with detailed experimental studies. The results show that the proposed strategy effectively generates a multilevel voltage waveform at the load terminals. Furthermore, the performance of the proposed approach is compared with the traditional phase-shifted carrier pulse width modulation schemes. In addition, it can be easily applied to the n-level modular multilevel converter with any type of submodule configuration.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.049
GPT teacher head0.249
Teacher spread0.201 · 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 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

Citations33
Published2020
Admission routes2
Has abstractyes

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