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Record W1527300519 · doi:10.1109/iecon.2014.7049274

Active capacitor implementation using nonlinear state-space model of bidirectional buck and boost converter

2014· article· en· W1527300519 on OpenAlexaff
Ali Shagerdmootaab, Mehrdad Moallem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCapacitorBuck converterNonlinear systemComputer scienceState spaceBuck–boost converterControl theory (sociology)InductorElectronic engineeringPhysicsElectrical engineeringVoltageEngineeringMathematicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

In this paper, the development of a novel controller for a bidirectional buck and boost converter is studied to implement an active capacitance. To this end, an averaging method is used to obtain a nonlinear state-space model of the converter in the Continuous Conduction Mode (CCM) for buck and boost configurations, separately. These models are then combined into a state-space representation for the whole range of operation. Using the obtained model, a controller is designed to track a desired input current based on the applied input voltage such that the circuit exhibits a capacitive effect between the input terminals. The converter along with its proposed control method is a suitable replacement for the commonly used electrolytic capacitors in different applications. Simulation results are presented that demonstrate performance of the proposed current control method. Experimental verification is currently under investigation.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.008

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.249
Teacher spread0.234 · 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
GenreMethods

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

Citations2
Published2014
Admission routes1
Has abstractyes

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