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Record W3195959125 · doi:10.3390/en14165158

Modeling of Average Current in Ideal and Non-Ideal Boost and Synchronous Boost Converters

2021· article· en· W3195959125 on OpenAlexaff
Sumukh Surya, Sheldon S. Williamson

Bibliographic record

VenueEnergies · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsConvertersControl theory (sociology)MATLABInductorTransfer functionDuty cycleIdeal (ethics)Boost converterElectronic engineeringComputer scienceVoltageEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper provides a modeling approach for average current control (ACC) operating in open-loop configuration. The converters chosen are non-ideal boost and synchronous boost converters operating in continuous conduction mode (CCM). Initially, these converters are mathematically modeled considering all the non-idealities using volt-sec and amp-sec balance equations and simulated using MATLAB and Simulink. The open-loop transfer function of the switch current or inductor current (Gid) to the duty ratio is derived using the state space averaging (SSA) technique and analyzed using MATLAB/Simulink. It is observed that the Gid of the converters is highly stable in open loop. A larger magnitude resonance is observed in ideal boost and synchronous boost converters than the non-ideal converters. However, the low frequency gain and the crossover frequency remained the same. With the increase in the load resistance, higher resonance and lower low frequency gain is observed in non-ideal boost and non-ideal boost synchronous boost converters. The derived transfer function is validated against the standard switch model using LTSpice software.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.698

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 head0.006
GPT teacher head0.211
Teacher spread0.204 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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