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Record W2992674332 · doi:10.1109/tie.2019.2956372

Generalized Phase-Shift PWM for Active-Neutral-Point-Clamped Multilevel Converter

2019· article· en· W2992674332 on OpenAlexafffund
Yuzhuo Li, Hao Tian, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsPulse-width modulationModular designConvertersNetwork topologyTopology (electrical circuits)ScalabilityComputer scienceElectronic engineeringControl theory (sociology)Modulation (music)EngineeringVoltageControl (management)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

The multilevel active-neutral-point-clamped (ANPC) converters have more control freedoms compared to the conventional neutral-point-clamped topology due to increased switching redundancies. However, the device utilization of level-shift pulsewidth modulation (PWM) is inherently low, and the scalability of space-vector-based PWM is challenging to realize for high-level (e.g., four-level or five-level) topologies. In this article, a novel phase-shift PWM and the design approach for ANPC converter are proposed to handle these two problems simultaneously. The proposed idea enables the modular phase-shift PWM design based on the switch group concept. In such a way, the complexity in the PWM design process can be significantly reduced with enhanced scalability for both the general and simplified ANPC topologies. Moreover, compared with conventional level-shift PWM, the proposed PWM schemes can fully utilize the devices for better loss distribution and higher equivalent switching frequency. Study cases have been done regarding output features, device utilizations, loss distributions, etc., through experimental verification and thorough discussions.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.035
GPT teacher head0.257
Teacher spread0.222 · 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 designOther design
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

Citations20
Published2019
Admission routes2
Has abstractyes

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