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Record W2904006150 · doi:10.1109/ecce.2018.8557652

A SiC-based, Fully Soft-Switched Bridge-less AC/DC Converter with High Voltage Conversion Ratio Based on Current Fed Voltage Quadrupler Modules for MVDC Conversion in Wind Energy Application

2018· article· en· W2904006150 on OpenAlexaff
Mehdi Abbasi, John Lam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsYork University
Fundersnot available
KeywordsRectifier (neural networks)Topology (electrical circuits)High voltageVoltageDiodeElectrical engineeringController (irrigation)Computer scienceSilicon carbideElectronic engineeringPhysicsMaterials scienceEngineeringBiology

Abstract

fetched live from OpenAlex

In this paper a novel soft-switched, bridgeless rectifier is presented to be integrated with an isolated step-up resonant converter for step-up voltage conversion in medium voltage (MV) DC grid in wind energy application. To reduce the number of conversion stages, the proposed bridgeless boost rectifier is integrated with a high-frequency step-up resonant converter with voltage quadrupler modules to form a single-stage AC/DC step-up converter. The proposed converter is able to reduce the number of semiconductors required for the front-end rectifier, thus simplifying the resulting controller. All the switches and diodes are able to achieve soft-switching to enhance the circuit efficiency. Simulation results are given on a 1MW, 690Vac/23kVDCsystem to highlight the merits of the proposed converter. Experimental results are also provided on a laboratory scale Silicon Carbide (SiC)-based 100VL-L/1.6kVDCproof of concept prototype.

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 score0.003
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 communication0.0010.001
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 head0.010
GPT teacher head0.219
Teacher spread0.208 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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