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Record W4286373664 · doi:10.1109/access.2022.3192656

Magnetically Coupled Single-Phase AC-AC Converter With Reduced Number of Passive Components

2022· article· en· W4286373664 on OpenAlexaff
S. Esmaeili, Erfan Azimi, Hossein Hafezi, Amin Mahmoudi, Mohsin Jamil, Ashraf Ali Khan

Bibliographic record

VenueIEEE Access · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsTopology (electrical circuits)Duty cycleElectrical impedanceVoltageComputer scienceSnubberConvertersElectronic engineeringControl theory (sociology)Electrical engineeringCapacitorEngineering

Abstract

fetched live from OpenAlex

An AC-AC direct single-phase converter based on an impedance network is presented in this manuscript. It possesses all privileges of similar impedance source AC-AC converters such as buck-boost ability, maintaining or reversing the phase angle and sharing the same ground between input and output voltage. Furthermore, a magnetic coupling is exploited to provide high voltage gain by adjusting its turns ratio along with the duty cycle. A safe commutation strategy is implemented to avoid current and voltage spikes across switches needless to utilize snubber circuits. The presented converter offers continuous input current, and ac to ac conversion is done directly without using dc storage, making it appropriate for dynamic voltage restorer to compensate voltage sags and voltage swells. In addition, LC input and output filters are eliminated thanks to impedance network structure. In this regard, the presented topology offers good features in size and cost by reducing passive components compared to similar structures. Also, the comparative investigation shows that the proposed converter benefits from superior operational ranges among similar well-known topologies for the same conditions. Finally, theoretical analyses and operation modes of the proposed converter are discussed and testified by both simulation and experimental results.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.272
Teacher spread0.242 · 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

Citations17
Published2022
Admission routes1
Has abstractyes

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