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Record W3080577293 · doi:10.1109/cjece.2020.2967129

Four-Channel Buck Converter in Asymmetrical Continuous Current Conduction Mode

2020· article· en· W3080577293 on OpenAlexvenueno aff
Lilla Litvani, J. Hamar

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsnot available
FundersEuropean Social Fund
KeywordsCurrent (fluid)Channel (broadcasting)Mode (computer interface)Thermal conductionBuck converterPhysicsElectrical engineeringMechanicsComputer scienceEngineeringVoltageThermodynamics

Abstract

fetched live from OpenAlex

This article is concerned with the asymmetrical control strategies of a four-channel dc/dc buck converter in the continuous current conduction mode (CCM). The converter has two input and four output channels. Energy equations of the converter are studied, and asymmetry of input voltages, output voltages, and loads are examined in the CCM. The proposed control strategy allows us to directly control the power flow in each channel without any unwanted consequences for the rest of the channels among these conditions. The main advantage is the direct control of the total transferred power by the positive sequence of the capacitor voltage, the power exchange among the input channels by the negative sequence of the capacitor voltage, and the power exchange among the output channels by the inductor currents. Calculating these currents also helps to determine whether the converter at a certain point operates in the CCM or in the boundary mode (where zero current switching is possible). Feasibility of zero-voltage switching is simplified by this method. Converter efficiency is showed to be 83% at asymmetrical output voltages in the CCM. Theoretical studies are confirmed by both numerical simulations and laboratory experiments on a 100-W prototype. The studied converter can be applied in supplying multilevel inverters.

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 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 score0.847
Threshold uncertainty score0.649

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 head0.018
GPT teacher head0.185
Teacher spread0.167 · 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 teacher head, 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
Published2020
Admission routes1
Has abstractyes

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