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Record W2523687811 · doi:10.11159/eee16.104

A Simplified Predictive Current Control for Matrix Converters with Reduced Common-mode Voltage

2016· article· en· W2523687811 on OpenAlexvenueno aff
Thanh-Luan Nguyen, Hong‐Hee Lee, Tae-Won Chun

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2016
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsnot available
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsConvertersCurrent (fluid)Common-mode signalControl theory (sociology)Model predictive controlVoltageComputer scienceMode (computer interface)Matrix (chemical analysis)Control (management)Electronic engineeringEngineeringElectrical engineeringMaterials scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Predictive current control (PCC) is an effective method to control matrix converters (MCs) due to its advantages such as simplicity, fast dynamic response, and flexibility to control different variables.However, the high amount of calculations for the PCC is an obstacle for its real application.To overcome this problem, this paper proposes a simplified PCC method for MCs by utilizing the sector distribution.In addition, the proposed method reduces the peak value of common-mode voltage (CMV) to 42% by using a mediumvalued phase voltage to generate the zero vectors.Simulation results are shown to validate the effectiveness of the proposed PCC 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 score0.003
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 communication0.0010.000
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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