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Series LC Resonance-Pulse Based Zero-Current-Switching Current-Fed Half-Bridge DC-DC Converter

2020· article· en· W3037049020 on OpenAlexaff
Swati Tandon, Akshay Kumar Rathore

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsCommutationCapacitorInductanceLeakage inductanceTransformerSnubberForward converterConvertersVoltageRLC circuitDC biasBoost converterComputer scienceElectronic engineeringElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

This paper proposes a novel zero-current switching (ZCS) current-fed half-bridge DC-DC converter for high voltage gain applications. The proposed converter enables soft-commutation at device turn-off by utilizing resonance between transformer leakage inductance and a series capacitor placed on the primary side of the high frequency transformer. Existing current-fed converters experience turn-off voltage spikes and uses several methods to achieve soft switching which further increases circulating current and limit soft commutation at light-loads. To address these limitations, series LC resonance pulse structure has been proposed where variable frequency control with variable duty is employed to achieve ZCS operation over wide range of load and input variations. Operation of the proposed converter is presented and validated with simulation and experimental results for 500W laboratory prototype.

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 score0.002
Threshold uncertainty score0.008

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.242
Teacher spread0.219 · 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
Published2020
Admission routes1
Has abstractyes

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