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Record W4383899501 · doi:10.1109/access.2023.3294270

A New Approach to Reduce DC-Bus Capacitance in Regenerative CHB Motor Drives

2023· article· en· W4383899501 on OpenAlexafffund
Ahmed Abuelnaga, Zhituo Ni, Sarah Badawi, Mehdi Narimani, Navid R. Zargari

Bibliographic record

VenueIEEE Access · 2023
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster UniversityRockwell Automation (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConvertersPWM rectifierComputer sciencePulse-width modulationCapacitanceHarmonicsPower (physics)RippleController (irrigation)VoltageElectrical engineeringElectronic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

The cascaded H-bridge (CHB) converters with diode-front-end (DFE) structure have been widely adopted in non-regenerative high-power medium-voltage motor drives. In order to allow regenerative capability, the diode-front end (DFE) in each power cell can be replaced by an IGBT-based active-front-end (AFE) operating as a PWM rectifier. Due to the inherent instantaneous power unbalance in the AFE-based CHB power cells, the DC-bus capacitance needs to be increased to reduce DC-bus voltage ripples. To reduce the DC-bus capacitance, control degrees of freedom provided by the PWM rectifiers can be exploited to mitigate the DC-bus voltage ripples. This paper proposes a new controller for regenerative CHB converters to perform DC-bus voltage control and ripple mitigation while maintaining high dynamic performance and canceling low order harmonics at the grid side to meet the grid standards. The effectiveness of the proposed control scheme is validated experimentally on a seven-level regenerative CHB drive.

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 score0.576
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.052
GPT teacher head0.285
Teacher spread0.233 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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