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Record W3127599338 · doi:10.1049/pel2.12028

Novel high voltage gain dc–dc converter with dynamic analysis

2021· article· en· W3127599338 on OpenAlexaff
Mohammad Ghaffarpour, Reza Ebrahimi, Hossein Madadi Kojabadi, Liuchen Chang, Josep M. Guerrero

Bibliographic record

VenueIET Power Electronics · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
Fundersnot available
KeywordsBoost converterDuty cycleTopology (electrical circuits)Forward converterPulse-width modulationĆuk converterFlyback converterElectronic engineeringBuck converterCapacitorInductorBuck–boost converterComputer scienceVoltageEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Abstract This study proposes a non‐isolated dc–dc converter based on a coupled inductor and switch‐capacitor–boosting techniques. The goal of the proposed topology is to increase the voltage gain with low‐duty cycle, so voltage stresses are decreased across power switches and diodes. The power switches are controlled by pulse width modulation. The common ground of input and output of the proposed converter makes it suitable for many applications such as photovoltaic systems. The proposed converter topology is discussed in continuous and discontinuous operation modes. A dynamic analysis of the proposed converter is also provided. Simulation and experimental verifications are presented to prove the effectiveness of the proposed topology.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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
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

Citations6
Published2021
Admission routes1
Has abstractyes

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