MétaCan
Menu
Back to cohort
Record W4312999961 · doi:10.1109/tpel.2022.3228482

A Modulation Scheme Based on Virtual Voltage Levels for Capacitor Voltage Balancing of the Four-Level Diode Clamped Converter

2022· article· en· W4312999961 on OpenAlexafffund
Javad Ebrahimi, Shima Shahnooshi, Suzan Eren, Alireza Bakhshai

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centre of Innovation
KeywordsVoltageSpace vector modulationCapacitorModulation (music)Control theory (sociology)Electronic engineeringComputer scienceDiodeEngineeringTopology (electrical circuits)Pulse-width modulationElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This article proposes a modulation scheme based on the concept of virtual voltage levels for regulating the dc-link capacitor voltages of a four-level diode clamped converter under restricted operating conditions. The proposed virtual-level space vector modulation (SVM) scheme sets the average neutral point currents to zero, which results in no net change in dc-link capacitor voltages and thereby voltage balancing. In addition, a closed-loop algorithm is developed to compensate for any voltage drift due to unideal practical conditions. Consequently, the modulation of the reference voltages results in the desired output voltages while regulating the dc-link capacitor voltages at various operating points. In the proposed modulation scheme, switching frequencies are not significantly increased, and they are lower than those in the classic virtual-vector SVM method. The simulation results demonstrate the effectiveness of the proposed modulation scheme and highlight its main benefits and features. The simulation results are verified through a laboratory-type experimental setup.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.214
Teacher spread0.193 · 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 teacher head, not a consensus.

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

Citations25
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Power ElectronicsSame topicMultilevel Inverters and ConvertersFrench-language works237,207