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Record W3004751366 · doi:10.1109/tie.2020.2969096

A ∼99% <i>η</i> Hybrid Resonant/Coupled ZCS-Voltage-Quadruplers MV SiC Converter Module for DC Grid in Wind Systems

2020· article· en· W3004751366 on OpenAlexafffund
Mehdi Abbasi, John Lam

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransformerSilicon carbideInductorVoltageElectrical engineeringHigh voltageVoltage doublerDiodeBoost converterEngineeringElectronic engineeringMaterials scienceVoltage regulationDropout voltage

Abstract

fetched live from OpenAlex

In this article, a hybrid high voltage gain converter module for medium voltage dc conversion in wind energy systems is proposed. The converter consists of step-up resonant circuit with coupled voltage-quadruplers to achieve the step-up voltage conversion function. The coupled voltage-quadrupler modules are connected to the resonant circuit via a 1:1:1 high frequency transformer. As a result, high turns ratio transformers are not required in the proposed design while at the same time, the output voltage of each voltage-quadrupler is well balanced due to the shared coupled-inductor. All the primary side silicon carbide (SiC) MOSFETs and secondary side SiC diodes exhibit zero switching losses. The output voltage of the converter module is controlled with variable frequency control. Simulation results on a 4 MW modular design of SiC-based converter system with 3.3 kV/18 kV, 670 kW per module, and experimental results on a laboratory-scale prototype with per-module rating: 500 V/2.7 kV, 3.7 kW are provided to validate the theoretical analysis and to highlight the merits of the proposed work. Results confirmed that a peak efficiency of close to 99% is achieved in both simulation and experimental works.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.220
Teacher spread0.194 · 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 teacher head, not a consensus.

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

Citations12
Published2020
Admission routes2
Has abstractyes

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