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Record W2953581524 · doi:10.1109/tpel.2019.2926174

Current Control of a Seven-Level Voltage Source Inverter

2019· article· en· W2953581524 on OpenAlexafffund
Ahoora Bahrami, Mehdi Narimani, Margarita Norambuena, José Rodríguez

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNetwork topologyConvertersTopology (electrical circuits)CapacitorTransformerPower electronicsElectronic engineeringInverterVoltageComputer scienceControl theory (sociology)EngineeringControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

Demanding higher power, higher power quality, lower switching loss, and eliminating interface transformers in industrial applications caused significant attention toward multilevel converters. This paper presents current control of a seven-level topology for medium-voltage high-power applications. This topology has fewer active switches and components, and less control complexity in comparison to the other existing classic and advanced seven-level topologies. A comprehensive review has been done on seven-level topologies to demonstrate the advantages of this topology in this paper. A control method based on model predictive control (MPC) is developed to balance the flying capacitors of this topology at their desired values and control the output currents without the need of pulsewidth modulation blocks, and proportional-integral (PI) controllers, which is the advantage of MPC in power electronics applications. In order to evaluate the performance of the seven-level topology and the developed control method effectiveness, experimental results are shown for different conditions, which demonstrate the feasibility of the seven-level topology and the developed control technique.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
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 integrity0.0000.000
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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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

Citations23
Published2019
Admission routes2
Has abstractyes

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