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Record W4401507739 · doi:10.1109/tpel.2024.3441610

Dual 3L-NPC Converter System With Improved Power Quality for Bipolar DC Distribution

2024· article· en· W4401507739 on OpenAlexaff
Bowei Li, Xuesong Wu, Gregory J. Kish, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConvertersRippleTransformerForward converterInterfacingElectronic engineeringElectrical engineeringEngineeringFlyback converterVoltageBuck–boost converterComputer scienceTopology (electrical circuits)Control theory (sociology)Boost converterControl (management)

Abstract

fetched live from OpenAlex

Three-level neutral point clamped (3L-NPC) converters are a favorable candidate for the grid interfacing converter stage in bipolar dc distribution systems. This article proposes the use of dual 3L-NPCs where, by virtue of connecting their ac ports in a differential fashion across a center-tapped transformer winding, two main benefits can be realized: 1) full bipolar voltage balancing capability that can accommodate any degree of imbalance between two dc poles, and 2) improved dc-side power quality in terms of reduced ripples in the pole voltages and currents. The proposed converter system also avoids the reliance on more complex zigzag transformer arrangements used in prior art. Detailed theoretical analyses of the pole voltage balancing and the ripple reduction mechanisms are conducted, with the results guiding the development of a suitable control strategy. The advantages of the proposed scheme and the correctness of the theoretical analyses are validated through both simulation and experimental results.

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 score0.987
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 integrity0.0000.000
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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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