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Record W1558847321 · doi:10.1109/icit.2015.7125475

A new five-level buck-boost active rectifier

2015· article· en· W1558847321 on OpenAlexaff
Hani Vahedi, Philippe‐Alexandre Labbé, Hadi Y. Kanaan, Handy Fortin Blanchette, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsRectifier (neural networks)Precision rectifierPower factorCapacitorVoltageControl theory (sociology)Computer sciencePeak inverse voltageAC powerController (irrigation)Buck–boost converterTopology (electrical circuits)Power (physics)Boost converterElectronic engineeringElectrical engineeringEngineeringPhysicsVoltage optimisationControl (management)

Abstract

fetched live from OpenAlex

In this paper a new single-phase five-level buck-boost active rectifier is introduced called capacitor tied switches (CTS). The proposed rectifier has two independent DC outputs that can be connected to two different loads. Different switching states and the average mode of the proposed topology are analyzed to design the associated controller aims at regulating the two output DC voltages, generating five-level voltage at the input of the rectifier and finally draw unity power factor and sinusoidal current from AC grid. From AC grid view, the rectifier works in boost mode however the generated DC voltage can be split into two separate outputs which may be less than the AC peak voltage or even more leads to work in both buck and boost operation mode. Full simulation results are shown and analyzed to validate the effective operation and good dynamic performance of the proposed five-level buck-boost rectifier.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.057
GPT teacher head0.232
Teacher spread0.175 · 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
GenreMethods

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

Citations29
Published2015
Admission routes1
Has abstractyes

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