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A Minimum Power Loss Approach for Selecting the Turns Ratio of a Tapped Inductor and Mode of Operation of a 5-switch Bidirectional DC-DC Converter

2021· article· en· W3188242848 on OpenAlexaff
Gabriel R. Broday, Luiz A. C. Lopes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsInductorConvertersDuty cycleElectrical engineeringVoltagePower (physics)Buck converterBoost converterComputer scienceElectronic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

One of the most important aspects for the reliable operation of a DC Microgrid is the DC-bus voltage regulation within the system. For this, Supercapacitors (SCs) are widely used as auxiliary devices since they can provide sudden bursts of power and demand/supply currents with high gradients. Since commercial SCs usually present low voltage levels and, with the increasing trend of raising the DC-bus voltage, their connection in such systems requires power electronics interfaces capable of providing a wide range of voltage conversion. In this scenario, Tapped Inductors (TIs) provide a means for achieving low/high voltage gains in non-isolated DC-DC converters with neither too small nor too large duty cycles (D) by adjusting their turns ratio (n:1) to a desired value at a given operating point. However, a proper methodology for the actual choice of this value and the impacts of this on important aspects of the system, such as the converter efficiency, are still lacking in the literature. This way, this paper concerns a 5-switch DC-DC converter, based on a TI and that presents voltage gains that vary with D with Buck, Boost and Buck-Boost characteristics, where the main objective is to present an approach to determine the mode of operation and the turns ratio of the TI for reducing the losses on the power switches.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.734
Threshold uncertainty score0.216

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 head0.008
GPT teacher head0.205
Teacher spread0.198 · 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 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

Citations4
Published2021
Admission routes1
Has abstractyes

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