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Record W2896022874 · doi:10.1109/tpel.2018.2876337

LED Driver Achieves Electrolytic Capacitor-Less and Flicker-Free Operation With an Energy Buffer Unit

2018· article· en· W2896022874 on OpenAlexafffund
Peng Fang, Bo Sheng, Samuel Webb, Yan Zhang, Yan‐Fei Liu

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolytic capacitorCapacitorEnergy storageElectrical engineeringFilm capacitorPower (physics)VoltagePower factorEngineeringAutomotive engineeringComputer scienceElectronic engineering

Abstract

fetched live from OpenAlex

Electrolytic capacitors are often needed to provide the high-density energy storage required by ac-powered LED drivers; however, they are also well known for their short lifespans. Eliminating electrolytic capacitors in ac-dc LED driver design has become a very important target to improve LED driver technology. In this paper, a cycle-by-cycle energy buffering LED driver has been proposed to achieve electrolytic capacitor-less flicker-free operation. An energy buffering unit, with high-voltage film capacitors being the energy storage device, is introduced in the design to buffer the imbalanced energy in every switching cycle. The switching current can be controlled to meet high power factor correction requirement, while maintaining dc LED output current at the same time. Compared to previous electrolytic capacitor-less designs, this technology can reduce circulating power and, therefore, power conversion loss. A 15-W experimental prototype had been built and tested to verify the proposed LED driving method.

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: Empirical
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)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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

Citations44
Published2018
Admission routes2
Has abstractyes

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