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The Efficiency Enhancement of a Single-phase Single-stage Buck-boost type Manitoba Inverter Using SiC MOSFETs for Residential PV Applications

2020· article· en· W3134869532 on OpenAlexafffundabout
Yanming Xu, Carl Ngai Man Ho, Ken King Man Siu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of Manitoba
FundersResearch Manitoba
KeywordsInverterMOSFETSilicon carbidePhotovoltaic systemElectronic engineeringConvertersMaterials sciencePower semiconductor devicePower (physics)Electrical engineeringComputer scienceVoltageEngineeringTransistorPhysics

Abstract

fetched live from OpenAlex

One of the advantages of Silicon Carbide (SiC) MOSFET is high breakdown voltage with low switching losses, this leads single-stage buck-boost type converters using SiC devices becoming attractive for industrial applications. This paper presents a mixed combination of SiC MOSFETs, Si-IGBTs, and Si-MOSFETs in a single-stage buck-boost type grid-connected inverter for Photovoltaic (PV) applications to achieve low power losses and high efficiency. In order to identify the most suitable semiconductor combination for the required power rating and switching frequency in this inverter, a comparative research on the static characteristic of SiC MOSFETs and Si-based devices is conducted. Moreover, the power losses of various devices are further estimated by mathematical power loss model. Finally, an experimental evaluation is carried out in a 1.2kW inverter prototype. The results indicate that the use of the proposed mixed combination can benefit PV inverter with up to 4% higher system efficiency comparing with all-Si-based system and the advantages of various devices can be fully utilized.

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 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 score0.093
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.068
GPT teacher head0.280
Teacher spread0.212 · 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.

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".

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Citations2
Published2020
Admission routes3
Has abstractyes

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