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Record W2915089099 · doi:10.1109/tpel.2019.2900019

Phase-Disposition PWM Based 2DoF-Interleaving Scheme for Minimizing High Frequency ZSCC in Modular Parallel Three-Level Converters

2019· article· en· W2915089099 on OpenAlexafffund
Zhongyi Quan, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInterleavingConvertersModular designElectronic engineeringComputer scienceControl theory (sociology)Pulse-width modulationVoltageEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper proposes a two-degree-of-freedom (2DoF) interleaving scheme based on phase-disposition (PD) PWM for minimizing the high-frequency zero-sequence circulating current (ZSCC) in modular interleaved three-level converters. The method incorporates an internal interleaving by phase-shifting the carriers in each individual converter, and an external interleaving by phase-shifting the carriers of the different converters. A general harmonic distribution analysis which is valid for all types of voltage source converters is carried out to simplify the evaluation of the proposed method. A comprehensive evaluation of the proposed method shows that the PD-2DoF interleaving scheme produces the lowest ZSCC maximum value as compared to existing approaches. Moreover, it can achieve comparable performances as existing methods do in terms of output quality and common mode voltage magnitude, showing a great potential for practical implementation. Simulation and experimental results are obtained to demonstrate the performance of the proposed method. Generalization of the 2DoF interleaving scheme to high-level converter is also discussed in this paper.

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: none
Teacher disagreement score0.818
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.230
Teacher spread0.215 · 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

Citations36
Published2019
Admission routes2
Has abstractyes

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