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Common-Mode Voltages Reduction Space Vector Modulation for Active Neutral-Point-Clamped converter

2021· article· en· W3215448096 on OpenAlexaff
Jalal Amini, Mehrdad Moallem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCommon-mode signalReduction (mathematics)ConvertersModulation (music)Point (geometry)EMIComputer scienceInverterVoltageSpace vector modulationElectronic engineeringFrame (networking)Projection (relational algebra)Reliability (semiconductor)Control theory (sociology)EngineeringElectromagnetic interferencePower (physics)AlgorithmMathematicsPhysicsElectrical engineeringControl (management)Telecommunications

Abstract

fetched live from OpenAlex

Common-mode voltage (CMV), generated by converters, has adverse effects in many applications including EMI and reliability issues. Multilevel inverters reduce dv/dt of CMV; however, the magnitude of CMV can be comparable to the one generated by a conventional two-level inverter. In this paper, a new space vector modulation method for multilevel inverters is proposed to reduce the CMV without extra hardware. To make the modulation method facile, the proposed method utilizes a 60°-frame with all vectors mirrored to the first sextant. To this end, a new rotation formula in the 60°-frame is developed. The scheme is suitable for any multilevel configuration with any number of levels. To study its feasibility, the method is utilized to control a five-level active neutral-point-clamped converter. Simulation results are presented to demonstrate the feasibility and effectiveness of the proposed scheme in CMV reduction.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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 designNot applicable
Domainnot available
GenreMethods

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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