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Record W4285192164 · doi:10.1109/tpel.2022.3178140

A High Voltage Gain ZVT Quasi-Z-Source Converter With Reduced Voltage Stress

2022· article· en· W4285192164 on OpenAlexaff
Milad Heidari, Morteza Esteki, S. Ali Khajehoddin, Hosein Farzanehfard

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBoost converterNegative impedance converterInductorIntegrating ADCĆuk converterBuck–boost converterCapacitorElectrical engineeringElectronic engineeringBuck converterVoltage dividerVoltageEngineering

Abstract

fetched live from OpenAlex

In this article, a new high voltage gain quasi-Z-source (QZS) dc–dc converter suitable for renewable energy applications is presented. The proposed converter utilizes a zero voltage transition auxiliary circuit with coupled inductors to provide soft-switching conditions for all switches for a wide range of output power. In addition, a switched capacitor circuit is employed to obtain a higher voltage gain and lower voltage stress. The main advantages of the proposed converter include high voltage gain, high efficiency, reduced switch and diode voltage stress, continuous input current, and common ground between the output and input, which are important features for photovoltaic applications. In this circuit, the reverse recovery problem of diodes is alleviated. Operating principles and design considerations of the proposed converter are analyzed. Moreover, to prove the validity of the theoretical analysis, a 200-W prototype of the proposed converter is implemented and the experimental results are shown.

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 score0.002
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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.004
GPT teacher head0.191
Teacher spread0.187 · 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

Citations23
Published2022
Admission routes1
Has abstractyes

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