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Voltage Balancing Feature and Output Regulation in a Multi-Mode Inverter/Rectifier Leg

2022· article· en· W4280605626 on OpenAlexaff
Reza Emamalipour, John S. Lam

Bibliographic record

Venue2022 IEEE Applied Power Electronics Conference and Exposition (APEC) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsYork University
Fundersnot available
KeywordsCapacitorConvertersVoltageInverterRectifier (neural networks)Voltage regulationRedundancy (engineering)Computer scienceElectronic engineeringControl theory (sociology)EngineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

Output voltage regulation for different loading conditions while simultaneously addressing the unbalanced voltage distribution over the link capacitors stays one of challenges in the design of bidirectional converters. This paper proposes a comprehensive control scheme with different modes of operation to regulate the output voltage in a bidirectional DC/DC converter that employs a multi-mode stacked-switches inverter/rectifier leg while simultaneously providing voltage balancing across all the DC-link capacitors. With built-in circuit redundancy and using a hybrid control, the gain range is widely extended for various loading conditions with limited switching frequency range while the voltage stress over the DC-link capacitors is equally distributed. Results are provided on a 400VDC to 600VDC, 1kW bidirectional converter.

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)
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 score0.950
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 integrity0.0000.001
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 head0.008
GPT teacher head0.211
Teacher spread0.202 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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