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Record W2116025123 · doi:10.1109/tie.2010.2048833

Multidimensional Modulation Technique for Cascaded Multilevel Converters

2010· article· en· W2116025123 on OpenAlexaff
José I. Leon, Samir Kouro, Sergio Vázquez, Ramón Portillo, Leopoldo G. Franquelo, J.M. Carrasco, José Rodríguez

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2010
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsConvertersModulation (music)Total harmonic distortionElectronic engineeringFeed forwardComputer scienceVoltageH bridgeHarmonic analysisHarmonicPower (physics)Flexibility (engineering)Distortion (music)Control theory (sociology)Pulse-width modulationEngineeringMathematicsControl (management)Control engineeringElectrical engineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Multilevel cascaded H-bridge converters have found industrial application in the medium-voltage high-power range. In this paper, a generalized modulation technique for this type of converter based on a multidimensional control region is presented. Using the multidimensional control region, it is shown that all previous modulation techniques are particularized versions of the proposed method. Several possible solutions to develop a specific implementation of the modulation method are addressed in order to show the potential possibilities and the flexibility of the proposed technique. In addition, a feedforward version of this technique is also introduced to determine the switching sequence and the switching times, avoiding low harmonic distortion with unbalanced dc voltages. Experimental results are shown in order to validate the proposed concepts.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 head0.026
GPT teacher head0.238
Teacher spread0.211 · 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
GenreMethods

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

Citations166
Published2010
Admission routes1
Has abstractyes

Explore more

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