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Record W3043121355 · doi:10.1109/tpel.2020.3010178

Novel Series <i>LC</i> Resonance-Pulse-Based ZCS Current-Fed Full-Bridge DC–DC Converter: Analysis, Design, and Experimental Results

2020· article· en· W3043121355 on OpenAlexafffund
Swati Tandon, Akshay Kumar Rathore

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConvertersPulse-width modulationCommutationVoltageSemiconductor deviceCurrent (fluid)Resonance (particle physics)Pulse (music)Electronic engineeringCapacitorRLC circuitDuty cycleClampingElectrical engineeringSeries and parallel circuitsComputer scienceEngineeringMaterials sciencePhysics

Abstract

fetched live from OpenAlex

A current-fed full-bridge converter availing series resonance pulse to enable zero-current switching (ZCS) and voltage clamping of the semiconductor devices is proposed. Overlap in switching states of the devices enforces a short resonance pulse, due to series tank which naturally reduces the current to zero in the outgoing semiconductor devices. It causes zero-current commutation of the devices eliminating voltage spike across the semiconductor devices. Essentially, pulse-resonance offers reduced circulating current resulting in lower conduction losses and not demanding over-rated components. The proposed converter enables ZCS for wide variation in source voltage by implementing variable frequency fixed duty modulation eliminating the traditional requirement for additional clamping circuitry in conventional current-fed converters. Detailed experimental results on proof-of-concept hardware prototype rated at 500 W are demonstrated to verify the proposed claims and converter performance. With all other merits, proposed converter maintains high efficiency.

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 categoriesMeta-epidemiology (narrow)
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 score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.237
Teacher spread0.222 · 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.

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

Citations17
Published2020
Admission routes2
Has abstractyes

Explore more

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