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Record W2141072903 · doi:10.1109/iecon.2004.1431859

Comparison of control schemes for multilevel inverter with faulty cells

2005· article· en· W2141072903 on OpenAlexaff
Samnin Wei, Bin Wu, S. Rizzo, Navid R. Zargari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsRockwell Automation (Canada)Toronto Metropolitan University
Fundersnot available
KeywordsVoltageInverterPulse-width modulationControl theory (sociology)H bridgeComputer scienceSpace vectorLine (geometry)Topology (electrical circuits)Electronic engineeringControl (management)MathematicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Cascaded H-bridge multilevel inverter has been receiving wide attention mainly due to its capability of high voltage operation without switching devices connected in series. In real applications, one or more cell may fail and the output voltage is unbalanced, causing damaging results to load. In this paper, three control schemes including NS 1 and NS 2 based on neutral shift (NS) with delta injection and space vector (SV) PWM way, are compared in terms of the output line-to-line voltages THDs and their difference ratio. The reason of the difference is given for the first time, which has not been fully understood before. Based on this, a modified SV method is proposed. The theoretical results are also given, which shows that the NS has better performance than SV in theory. However, NS 1 and NS 2 are only approximately way to implement NS and they cannot achieve the best performance of NS, while modified SV has much better performance in terms of voltage difference ratio, which plays the most important role in a system with faulty cells. Simulation and experimental results are given to verify the algorithm.

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 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 score0.937
Threshold uncertainty score0.524

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 head0.022
GPT teacher head0.264
Teacher spread0.243 · 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.

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

Citations20
Published2005
Admission routes1
Has abstractyes

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