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Record W2807855001 · doi:10.1109/tie.2018.2844855

Current-Fed Isolated LCC-T Resonant Converter With ZCS and Improved Transformer Utilization

2018· article· en· W2807855001 on OpenAlexaff
Venkata R. Vakacharla, Akshay Kumar Rathore

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsTransformerInductorConvertersElectrical engineeringCapacitive sensingElectronic engineeringFlyback transformerDelta-wye transformerEnergy efficient transformerVoltageResonant converterCurrent transformerEngineering

Abstract

fetched live from OpenAlex

Resonant converters with capacitive output filters are quite popular for their higher voltage gain and compact designs. But the majority of the converters underutilize the transformer as they are subjected to discontinuous currents. This discontinuous current possesses huge current ripples that produce huge core losses and aggravate the temperature rise of the transformer. This often leads to saturation of the transformer. This paper presents an LCC-T resonant dc-dc converter with a capacitive output filter operating all switches in the zero current switching (ZCS) switching mode and whose transformer current is a continuous sinusoidal with minimum stress on resonant tank components. This is achieved by bringing the transformer in series to a resonant inductor. The proposed converter is simulated in power simulation (PSIM) 11.0.1. A proof-of-concept model rated 288 W is designed and developed, and hardware results are presented as verification to theory.

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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.001

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 head0.024
GPT teacher head0.240
Teacher spread0.216 · 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

Citations30
Published2018
Admission routes1
Has abstractyes

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