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Record W2937016105 · doi:10.3390/en12071376

A Single-Phase Buck and Boost AC-to-AC Converter with Bipolar Voltage Gain: Analysis, Design, and Implementation

2019· article· en· W2937016105 on OpenAlexaff
Naveed Ashraf, Tahir Izhar, Ghulam Abbas, Valentina Emilia Bălaş, Marius M. Bǎlaş, Tsung‐Chih Lin, Muhammad Usman Asad, Umar Farooq, Jason Gu

Bibliographic record

VenueEnergies · 2019
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSnubberBoost converterRippleBuck converterBuck–boost converterVoltageFlyback converterPulse-width modulationInductorControl theory (sociology)Electronic engineeringComputer scienceElectrical engineeringEngineeringCapacitorControl (management)

Abstract

fetched live from OpenAlex

In this research, a new single-phase direct AC-to-AC converter, operating in buck and boost mode, with a bipolar voltage gain, is proposed. The operation is accomplished through high frequency direct and indirect PWM control of a single switch with low voltage stresses. This reduces, not only the control effort, but also the switching losses. The low voltage stresses across the high frequency switches, reduce the dv/dt problem significantly without any loss and bulky voltage snubber arrangement. The operation, in its all-operating modes, has a low inductor ripple current and switching current. The proposed converter may be employed as an AC voltage restorer in a power distribution system to cope with the voltage sag and swell issues. The detailed analysis of the proposed converter is carried out in order to compare its performance with the existing converters. The simulation results obtained using the MATLAB/Simulink environment are verified through 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 categoriesnone
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 score0.840
Threshold uncertainty score0.630

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2019
Admission routes1
Has abstractyes

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