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A High Step-Up Multi-Mode Resonant Switched-Resonator Converter

2022· article· en· W4310450702 on OpenAlexaff
Dulika Nayanasiri, Yunwei Li, Cheng Xue

Bibliographic record

Venue2022 IEEE Energy Conversion Congress and Exposition (ECCE) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSwitched capacitorConvertersBuck converterResonatorPulse-width modulationElectronic engineeringBoost converterBuck–boost converterCapacitorĆuk converterVoltageComputer scienceEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Switched-resonator converter is one of the feasible options to achieve high power density and efficiency. The existing switched-resonator converters have limited voltage boosting capability. The output is further boosted by cascading conversion stages. Therefore, a high step-up switched-capacitor converter based on the multi-resonant and multi-mode operation is proposed in this article. The multi-resonant operation helps reduce switching losses in some switches because of the zero-current-switching. The converter output voltage is regulated in a wide range, modulating the pulse-width of the control signals using the multi-mode operation. It avoids challenges when designing a power converter system based on switching frequency modulation. The converter has a linear relationship between the duty ratio and voltage gain that cannot be observed in other switched resonator step-up converters. These properties of the converter are analyzed and validated using experimental results.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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