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Record W2563663961 · doi:10.1109/tpel.2016.2640662

A Transformerless Bipolar Modular Multilevel DC–DC Converter With Wide Voltage Ratios

2016· article· en· W2563663961 on OpenAlexaff
Sixing Du, Bin Wu

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2016
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsElectrical engineeringModular designPower (physics)VoltageElectronic engineeringForward converterEngineeringTopology (electrical circuits)Computer scienceBoost converterPhysics

Abstract

fetched live from OpenAlex

This paper presents a transformerless bipolar dc- dc converter based on series-connected submodules. It is intended for transforming dc voltage and managing power flow for medium/high-voltage dc grids. The dc-dc converter is composed of positive-pole and negative-pole subsystems, each of which consists of six arms/branches. Every two arms and one branch are constructed into a T-type circuit with the outer terminals interface to primary- and secondary-side dc buses. The two T-type circuits in one subsystem are connected in parallel to transmit power between primary and secondary sides. The transformerless structure (none bulky coupled inductors as well) solves the challengeable insulation and cooling issues of the extremely high-power magnetics in prior arts, while not sacrificing the power efficiency and system reliability. A ±10-kV 2-MW simulation model performed in MATLAB/Simulink verifies the feasibility of the bipolar dc-dc converter. Experimental results obtained from a 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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 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

Citations70
Published2016
Admission routes1
Has abstractyes

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