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Record W2982599110 · doi:10.1109/tie.2019.2944068

Fully Soft-Switched High Step-Up Nonisolated Three-Port DC–DC Converter Using GaN HEMTs

2019· article· en· W2982599110 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBoost converterĆuk converterForward converterInductorBuck–boost converterElectrical engineeringGallium nitrideElectronic engineeringFlyback converterVoltageEngineeringMaterials science

Abstract

fetched live from OpenAlex

In this article, a soft-switched nonisolated high step-up multi-input dc-dc converter is proposed. The proposed converter has overcome the hard switching problem of the conventional boost three port converter (boost-TPC) by providing zero-voltage-switching condition for all switches at various operating modes. The proposed converter uses coupled inductors technique to enhance the voltage gain and utilizes the leakage inductance energy and the energy storage device power path to provide soft switching condition. In addition, the voltage stress of the main switch is reduced which has led to utilizing low Rds(ON) switches. Various converter operating modes are presented and design considerations are discussed. To evaluate the proposed converter performance, two prototypes of the proposed converter are implemented utilizing the latest generation gallium nitride high electron mobility transistors and the mature Si MOSFETs technology. The results show that the proposed converter efficiency is enhanced in comparison with the conventional boost-TPC converter.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.772
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.223
Teacher spread0.203 · 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