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Record W2580606021 · doi:10.1109/tpel.2017.2660399

Start-Up Operation of a Modular Multilevel Converter With Flying Capacitor Submodules

2017· article· en· W2580606021 on OpenAlexaff
Apparao Dekka, Bin Wu, Navid R. Zargari

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2017
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsRockwell Automation (Canada)Toronto Metropolitan University
Fundersnot available
KeywordsModular designCapacitorElectrical engineeringComputer scienceEngineeringElectronic engineeringVoltageOperating system

Abstract

fetched live from OpenAlex

The three-level flying capacitor (3L-FC) submodule significantly reduces the magnitude of circulating currents, voltage ripple, and footprint size and improves the efficiency of the modular multilevel converter (MMC). Due to the above advantages, the 3L-FC submodule becomes an alternative for the conventional half-bridge submodule in the MMC. Each 3L-FC submodule consists of two floating capacitors with different nominal voltage rating. The precharging of floating capacitors without inrush current is one of the major challenges in the 3L-FC-based MMC. This paper proposes a sequence of design steps to precharge the floating capacitors in the 3L-FC-based MMC. The proposed approach is highly effective to charge the outer and inner capacitors of each 3L-FC submodule to their nominal value. The superiority of the proposed approach is verified through the MATLAB simulations and dSPACE/DS1103 experiments on a laboratory prototype of the 3L-FC-based MMC.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.013
GPT teacher head0.219
Teacher spread0.206 · 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

Citations35
Published2017
Admission routes1
Has abstractyes

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