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Record W2905290087 · doi:10.1109/ecce.2018.8557645

Single-Phase Bridge Inverter with Active Power Decoupling Based on Buck-Boost Converter

2018· article· en· W2905290087 on OpenAlexaff
Shuang Xu, Liuchen Chang, Riming Shao, AR Haider Mohomad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsDecoupling (probability)Buck–boost converterBoost converterBuck converterĆuk converterInverterForward converterPower electronicsElectronic engineeringComputer scienceCapacitorFlyback converterControl theory (sociology)VoltageElectrical engineeringEngineeringControl engineering

Abstract

fetched live from OpenAlex

Single-phase voltage-source bridge inverters usually suffer from low DC voltage utilization and power mismatch between the input and output sides. To tackle these issues, additional circuits and modulation techniques have been applied to the bridge inverters, but those methods generally add extra power electronics or complicate the control strategy. In this paper, a power decoupling method without additional power electronic components is proposed for a DC to single-phase AC converter, which adds only a small film capacitor to the front-end buck-boost converter and the voltage-source bridge inverter. The proposed topology is compared with single-phase bridge inverter with active power decoupling based on boost converter to show its pros and cons. Simulation and experimental results verified the feasibility of the proposed power decoupling method on the two-stage single-phase bridge inverter with buck-boost converter.

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)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.238
Teacher spread0.225 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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