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Record W2019621647 · doi:10.1109/itec.2013.6573490

Neutral-point voltage balancing of three-level EV traction inverter using modified carrier-based SVM and THD comparison with a two-level inverter

2013· article· en· W2019621647 on OpenAlexaff
Lekha Sejpal, Luiz A. C. Lopes, M. Amar, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsTotal harmonic distortionInverterControl theory (sociology)Space vector modulationTraction motorComputer scienceVoltagePulse-width modulationEngineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

This paper concentrates on the application of a three-level Neutral-Point Clamped (NPC) inverter in a Permanent Magnet Synchronous Motor (PMSM) driven electric vehicle. Carrier-Based Space Vector Modulation (CB-SVM) strategy allows a simple technique for inverter switching. A split-capacitor dc bus voltage balancing strategy using a simple PI controller has been presented for the entire speed range of operation of the PMSM. For the same current Total Harmonic Distortion (THD), three-level NPC inverter provides a 65% reduction in switching frequency when compared to a conventional two-level inverter while driving the PMSM with the same parameters. Torque and speed dual loop controllers have been developed using the Field-oriented Control technique to have a wider range of operation beyond the base speed of the PMSM.

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: Empirical
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.057
GPT teacher head0.240
Teacher spread0.183 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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