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A Novel Bidirectional Single-Phase Fifteen-Level Buck-Boost Rectifier for Power Factor Correction with Reduced Switch Count

2021· article· en· W3210727755 on OpenAlexaff
Amirabbas Kaymanesh, Ambrish Chandra, Catherine N. Mulligan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia UniversityÉcole de Technologie Supérieure
Fundersnot available
KeywordsRectifier (neural networks)Power factorPrecision rectifierWaveformControl theory (sociology)Peak inverse voltageComputer scienceVoltageAC powerTopology (electrical circuits)Three-phasePower (physics)Electronic engineeringEngineeringElectrical engineeringPhysicsVoltage optimisationControl (management)

Abstract

fetched live from OpenAlex

This paper introduces a novel bidirectional single-phase buck-boost rectifier for power factor correction that generates a fifteen-level voltage at its input. This multilevel rectifier as a high-power density topology with a reduced number of switches is based on the packed U-cell (PUC) converter. In this fifteen-level PUC-based rectifier (PUC15-rectifier), triple output terminals are available to supply various de loads at different voltages without requiring any dc-side passive filter. Besides, it is connected to the grid merely through a reduced size inductive filter. Generating a fifteen-level voltage waveform decreases the harmonic content of both voltage and current in the PUC15-rectifier input that makes it a reliable candidate for various industrial applications with a high-power quality performance. As PUC15-rectifier accurate operation depends on achieving four main control objectives and considering its dynamic model, a finite control-set model predictive control (FCS-MPC) method is also designed. The overall performance of the proposed PUC15-rectifier is verified by extensive simulation 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.863
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.251
Teacher spread0.205 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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