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Record W4413861904 · doi:10.1109/tie.2025.3595977

Dual-Buck Three-Phase Cyclo-Converters Without Commutation Problem

2025· article· en· W4413861904 on OpenAlexaff
Usman Ali Khan, Ashraf Ali Khan, Jung-Wook Park

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2025
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsConvertersCommutationControl theory (sociology)Commutation cellDual (grammatical number)Buck converterPhase (matter)Three-phaseComputer scienceElectronic engineeringTopology (electrical circuits)MathematicsEngineeringPhysicsElectrical engineeringVoltageSwitched-mode power supplyControl (management)

Abstract

fetched live from OpenAlex

This article proposes a novel three-phase dual-buck ac–ac converters capable of both step-up and step-down operations. They use a dual-buck structure, while effectively mitigating commutation problems and eliminating the necessity for lossy snubber circuits and complex soft commutation strategies. As a result, the output voltage with less distortion can be obtained. Also, the proposed converters provide flexibility in handling a wide range of input voltage requirements. Furthermore, they can significantly reduce the requirement for shoot-through inductors, which not only reduces component count but also enhances overall efficiency. A phase-shifted PWM strategy is utilized to enhance the effective switching frequency of inductors and capacitors while maintaining the same switching frequency of semiconductor devices while optimizing performance and reliability. Notably, the proposed converters utilize power metal-oxide-semiconductor field-effect transistors (MOSFETs) without engaging their body diodes, thereby resolving the reverse recovery issue typically associated with MOSFETs body diodes. To validate the feasibility of the proposed converters, both the simulation and experimental results of the proposed converters are presented, while demonstrating the converters superior performance and practical applicability.

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 score0.004
Threshold uncertainty score0.013

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.257
Teacher spread0.235 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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