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Record W2218844837 · doi:10.1109/vppc.2015.7352940

Efficient MMC Devices with Reduced Radiated and Conducted Interferences for Electric Vehicles Application

2015· article· en· W2218844837 on OpenAlexfundno aff
Luc-André Grégoire, Mohammad Sleiman, Handy Fortin Blanchette, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsnot available
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Research Chairs
KeywordsReliability (semiconductor)HarmonicTopology (electrical circuits)Modular designComputer scienceVoltageElectronic engineeringPower (physics)Electronic componentElectric vehicleActive filterElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a new cell module for modular multilevel converter (MMC) used in electric vehicles application. Traditionally, MMC has been used in high-power high-voltage application. The large number of power electronic devices required is rather used for reducing their voltage stress than the harmonic contents. Using the proposed cell topology, higher numbers of levels are achieved while the number of component remains low. This characteristic makes it an ideal configuration for a low switching high-efficiency AC/DC power converter, required for the soon to be common for plug-in vehicles application. Such converter can play two roles, not only can it be used as an active bidirectional charger, but also as active filter increasing network reliability. A predictive control algorithm is also presented in this paper, which will demonstrate the possibility to impose a charging curve on the DC side, even when the converter is used as active filter.

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 score0.898
Threshold uncertainty score0.210

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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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