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Ripple Analysis and Reduction for Neutral Line Current in a Single 3L-NPC Converter System for Bipolar DC Distribution

2025· article· W4416962606 on OpenAlexaff
Bowei Li, Xuesong Wu, Rui Liu, Gregory J. Kish, Yunwei Li

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRippleChokePulse-width modulationHarmonicsCurrent (fluid)Modulation (music)Total harmonic distortionControl theory (sociology)Harmonic

Abstract

fetched live from OpenAlex

As a cost-effective and structurally simple solution for bipolar dc distribution systems, three-level neutral point clamped (3L- NPC) converter systems employing a neutral line (NL) are challenged by power quality issues arising from significant NL current ripple. This work first investigates the formation mechanism of NL current ripple in a single NPC converter system. The harmonic distribution of the NL current is analyzed by deriving analytical expressions under both phase disposition (PD) and alternate phase opposition disposition (APOD) pulse width modulation (PWM) strategies. To suppress the significant NL current ripple observed with conventional PD-PWM, the carrier phase-shifting (CPS) strategy is incorporated to reconfigure the harmonic distribution in the common-mode (CM) domain. The resulting PD-CPS strategy effectively suppresses NL current ripple by reducing the peak CM currents, which also contributes to a smaller CM choke size and improved converter power density. Experimental results validate the theoretical analysis and demonstrate the effectiveness and advantages of the PD-CPS modulation strategy.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0000.001
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 head0.018
GPT teacher head0.252
Teacher spread0.234 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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