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Record W4403209950 · doi:10.1109/ojpel.2024.3476496

A Multilevel Current-Fed DAB Converter With Direct Power Transfer

2024· article· en· W4403209950 on OpenAlexaff
Sajjad Goudarzitaemeh, Lucas Melanson, Justin Woelfle, Majid Pahlevani

Bibliographic record

VenueIEEE Open Journal of Power Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsCurrent (fluid)Power (physics)Transfer (computing)Electrical engineeringComputer sciencePhysicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

This paper proposes a novel modulation scheme for a multilevel Dual Active Bridge (DAB) DC–DC converter with direct power transfer capability in PV applications. The proposed modulation scheme has the ability to shape the high-frequency current waveforms, leading to lower peak/RMS values of current over a wide range of PV voltages. In addition, the multi-level structure allows for the use of low-voltage devices with significantly smaller channel resistance. The power circuit topology is based on a current-fed half-bridge (CF-HB) converter, with a coupled inductor, which facilitates the direct power transfer, resulting in significantly lower conduction losses. In summary, the proposed topology tackles both the conduction and switching losses through a multi-faceted approach by using the novel modulation scheme, the multi-level structure, the direct power transfer, and the inherent soft-switching. Detailed mathematical analysis and extensive experimental results demonstrate the superior performance of the proposed 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 score0.010

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 communication0.0010.000
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 head0.010
GPT teacher head0.256
Teacher spread0.246 · 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
Published2024
Admission routes1
Has abstractyes

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