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Record W3109647816 · doi:10.1109/tie.2020.3036213

A Nonisolated TCM Bidirectional Converter With Low Input-Current-Ripple for DC Microgrids

2020· article· en· W3109647816 on OpenAlexaff
Mohammad Reza Mohammadi, Behzad Poorali, Suzan Eren, Majid Pahlevani

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's UniversityUniversity of Calgary
Fundersnot available
KeywordsRippleInductorConvertersTopology (electrical circuits)Boost converterĆuk converterVoltagePower (physics)Electronic engineeringBuck converterLow voltageBuck–boost converterElectrical engineeringForward converterControl theory (sociology)Computer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

This article presents a new nonisolated dc-dc converter with bidirectional power flow capability. The proposed converter operates in triangular conduction mode (TCM). Consequently, reverse-recovery losses are completely eliminated, zero voltage switching characteristics are obtained, and the inductor size can be reduced. However, in comparison with other converters in TCM that suffer from a large input-current-ripple, the proposed converter benefits from very low input-current-ripple. Furthermore, in the proposed converter, the voltage stress of the main switches is reduced, the converter voltage-gain is increased, the output current is nonpulsating, the input and output sources share a common ground, and only a single magnetic core is required. The proposed power circuit topology is theoretically analyzed and experimental results are provided to verify its feasibility and demonstrate its superior performance.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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

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 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

Citations26
Published2020
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Industrial ElectronicsSame topicAdvanced DC-DC ConvertersFrench-language works237,207