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Asymmetrically Modulated Three Port Bidirectional Current Fed-Push-Pull Converter for a DC Nanogrid

2021· article· en· W3183236528 on OpenAlexaff
Aniruddha Mukherjee, Ramtin Rasoulinezhad, Gerry Moschopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsWestern University
Fundersnot available
KeywordsTransformerForward converterĆuk converterPulse-width modulationBoost converterFlyback converterComputer scienceElectronic engineeringElectrical engineeringBuck–boost converterVoltageEngineering

Abstract

fetched live from OpenAlex

In this paper, a novel three-port bidirectional push-pull converter with an asymmetrical PWM half-bridge that is suitable for multi-energy source interfaced renewable energy systems in a DC nanogrid is proposed. The proposed converter can interface three sources with different voltage levels without a complex transformer and can achieve soft-switching without any auxiliary circuits or additional switches due to a modified asymmetrical PWM strategy. Moreover, the converter has a current-fed boost-type architecture that allows higher voltage gain to be achieved without additional circulating losses and its primary side has an additional input port connection that allows it to serve as a dual purpose half-bridge/boost converter to reduce overall system cost. In this paper, the operation of the proposed converter is explained, and simulation and experimental results of the proposed converter are presented.

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 score0.009

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 head0.017
GPT teacher head0.240
Teacher spread0.224 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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