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Record W2605328484 · doi:10.1109/tpwrd.2017.2692480

A Transformerless High-Voltage DC–DC Converter for DC Grid Interconnection

2017· article· en· W2605328484 on OpenAlexaff
Sixing Du, Bin Wu, Navid R. Zargari

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2017
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsRockwell Automation (Canada)Toronto Metropolitan University
Fundersnot available
KeywordsForward converterFlyback converterĆuk converterCharge pumpElectrical engineeringBoost converterVoltageElectronic engineeringBuck–boost converterComputer scienceEngineeringCapacitor

Abstract

fetched live from OpenAlex

This paper presents a transformerless high-voltage dc-dc converter based on cascaded sub-modules. It is intended for interconnecting high-voltage or medium-voltage dc grids. The proposed dc-dc converter consists of two phase legs, each of which consists of an upper arm and a lower arm with their middle nodes crossly connected through a branch of active sub-modules. By properly controlling the output voltage of the cross-connected branch, a trapezoidal ac current is induced to interact with the ac voltage components in the upper and lower arm for rebalancing the power amongst converter arms. The features of modular design, single-stage power conversion, and transformerless structure make the proposed dc-dc converter gain the outstanding merits of wide voltage ratio range, high system efficiency, and light converter weight. A control scheme is also elaborated for guaranteeing the normal operation of the proposed dc-dc converter. A 100-kV 100-MW simulation model performed in MATLAB/Simulink verifies the feasibility of the proposed dc-dc converter. Experimental results obtained from a 100-V 1-kW laboratory setup also confirm the validation of the proposal.

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 score0.004
Threshold uncertainty score0.012

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.228
Teacher spread0.213 · 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

Citations43
Published2017
Admission routes1
Has abstractyes

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