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Record W2480967173 · doi:10.1109/sta.2015.7505197

Implementation of a fast and simple space vector modulation scheme for a three-phase five-level neutral point clamped inverter

2015· article· en· W2480967173 on OpenAlexaff
Zouhaira Ben Mahmoud, Mahmoud Hamouda, Adel Khedher

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsInverterModulation (music)Point (geometry)Space vectorTopology (electrical circuits)AlgorithmComputer sciencePower (physics)Control theory (sociology)Three-phaseVoltageNetwork topologySpace vector modulationPhase (matter)Simple (philosophy)MathematicsEngineeringPhysicsArtificial intelligenceElectrical engineeringGeometry

Abstract

fetched live from OpenAlex

Multilevel inverters are nowadays considered among the most promising power conversion topologies for medium voltages and high power ranges. This work presents a fast space vector modulation algorithm (SVPWM) implemented on a three-phase five-level neutral point clamped (5L-NPC) inverter. The novelty of the proposed algorithm deals with the identification method of the adequate two-level small hexagon. This method computes the distance between each small hexagon center and the tip point of the reference space vector to be synthesized by the modulation algorithm. Therefore, the selected small hexagon is the one which has its center closest to the reference vector tip point. The remaining procedures needed to obtain the switching sequences of the transistors' gates are quite similar to those utilized by previous SVPWM algorithms. Computer simulations performed on a numerical model of the converter showed the effectiveness of the proposed identification 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 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: Empirical
Teacher disagreement score0.971
Threshold uncertainty score0.530

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 head0.054
GPT teacher head0.298
Teacher spread0.244 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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