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Record W2979992000 · doi:10.1109/sege.2019.8859887

Transformerless Grid-Connected Converters Using Active Virtual Ground Technique for Single-Phase Microgrids

2019· article· en· W2979992000 on OpenAlexaff
Ken King Man Siu, Carl Ngai Man Ho

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMicrogridConvertersNetwork topologyGridTopology (electrical circuits)Computer scienceElectronic engineeringSingle phaseRenewable energyPower (physics)Three-phaseElectrical engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

The paper presents the state-of-the-art technique Active Virtual Ground (AVG) in the design of single-phase grid-connected converters. The whole series of single-phase converters are based on the latest AVG technique. All of the designed topologies are with high efficiency, low leakage current, and single-stage structure. Focusing on the applications in ac microgrids, all of the evaluated topologies are classified into three different groups. They are associated with the three key components in the microgrid networks, which are power consumers, renewable energy sources and energy storage systems. The topology advantages and the design limitations of each converter type are analyzed in detail. The working principle of the AVG technique is demonstrated through the circuit equivalent model and is successfully verified in a set of 600 to 800 W prototypes. All of the experimental results are in good agreement with theoretical knowledge.

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 score0.002
Threshold uncertainty score0.006

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.223
Teacher spread0.213 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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