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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 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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.004
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 teacher head, not a consensus.

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

Citations23
Published2022
Admission routes1
Has abstractyes

Explore more

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