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Record W2798398883 · doi:10.1109/icit.2018.8352244

A new modular neutral point clamped converter with space vector modulation control

2018· article· en· W2798398883 on OpenAlexaff
Omid Beik, Apparao Dekka, Mehdi Narimani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSpace vector modulationConvertersModular designCapacitorModulation (music)VoltageSeries and parallel circuitsTopology (electrical circuits)Electronic engineeringControl theory (sociology)Computer sciencePower (physics)Transient (computer programming)EngineeringPulse-width modulationElectrical engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

This papers proposes a new 5-level modular neutral point clamped (MNPC) converter for medium voltage (MV) and high power applications. The proposed converter can operate at voltages up to 13.8kV using available commercial power semiconductor switches without series connection of the switches. The MNPC topology offers fewer numbers of components compared to the exiting five-level converters and simplicity in manufacturing as it can utilize off-the-shelf 3-level converter modules. In order to control the proposed 5-level converter, space vector modulation (SVM) technique is employed. Performance of the converter is evaluated at steady-state and transient conditions and for different modulation indices and loads by simulation studies. It is shown that using the SVM strategy the capacitor voltages are maintained around nominal values under both steady-state and transients.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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 designBench or experimental
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

Citations14
Published2018
Admission routes1
Has abstractyes

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