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Record W2027956470 · doi:10.1109/apec.2014.6803375

A high voltage-gain LLC micro-converter with high efficiency in wide input range for PV applications

2014· article· en· W2027956470 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsVoltage doublerVoltageRectifier (neural networks)DiodeElectrical engineeringHigh voltageBoost converterComputer scienceElectronic engineeringEngineeringVoltage regulationDropout voltage

Abstract

fetched live from OpenAlex

This paper proposes a novel high voltage-gain LLC micro-converter for PV applications. The converter has simple structure and minimum components with low cost. It can realize high voltage gain based on the voltage doubler rectifier with the optimal turns ratio. The main power switches can achieve ZVS and the output diodes can realize ZCS in wide input and load range. By utilizing the voltage doubler in the secondary side, the voltage stress over the output diodes can be reduced by half compared to the conventional center-tapped full-wave rectifier. A 24-48 V input, 380 V output and 200W prototype was built to verify the benefits of the proposed converter. The achieved efficiency of the converter peaks at 96.6% and the CEC weighted efficiency reaches 95.4%.

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.

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 categoriesnone
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 score0.940
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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

Quick stats

Citations49
Published2014
Admission routes1
Has abstractyes

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