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Record W2005786106 · doi:10.1109/pesgm.2012.6345069

An improved modulation scheme for harmonic distortion reduction in modular multilevel converter

2012· article· en· W2005786106 on OpenAlexaff
Ali Asghar Shojaei, G. Joós

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsMcGill University
Fundersnot available
KeywordsTotal harmonic distortionModular designReduction (mathematics)Modulation (music)THD analyzerDistortion (music)Scheme (mathematics)Electronic engineeringHarmonic analysisComputer scienceHarmonicTopology (electrical circuits)Nonlinear distortionPhysicsElectrical engineeringEngineeringMathematicsTelecommunicationsVoltageBandwidth (computing)Acoustics

Abstract

fetched live from OpenAlex

The modular multilevel converter (MMC) has recently attracted research attention in medium-voltage applications, such as MV drives and back-to-back systems. In such applications, the harmonic content of MMC's output voltage and current is of particular importance and must be kept below permissible levels. This is typically done by increasing the switching frequency and/or by using an output filter stage. To lower the total harmonic distortion (THD) of the output voltage and current at the terminals, in this work, a new modulation scheme for MMC is presented. Using this modulation scheme, since the voltage and current harmonics are lowered at the converter terminals, the rating and hence the cost of the output filter required to achieve permissible THD level is reduced. Simulation results are provided to testify to the effectiveness of the proposed method.

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 communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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

Citations22
Published2012
Admission routes1
Has abstractyes

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