MétaCan
Menu
Back to cohort
Record W4289912906 · doi:10.1109/access.2022.3196659

Review of Reduced Switch-Count Power Cells for Regenerative Cascaded H-Bridge Motor Drives

2022· article· en· W4289912906 on OpenAlexafffund
Doho Kang, Sarah Badawi, Zhituo Ni, Ahmed Abuelnaga, Mehdi Narimani, Navid R. Zargari

Bibliographic record

VenueIEEE Access · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsRockwell Automation (Canada)McMaster University
FundersNatural Sciences and Engineering Research Council of CanadaRockwell Automation
KeywordsH bridgeComputer scienceNetwork topologyScalabilityModularity (biology)Topology (electrical circuits)Electrical engineeringElectronic engineeringEngineeringVoltagePulse-width modulationComputer network

Abstract

fetched live from OpenAlex

Cascaded H-Bridge (CHB) topology is one of the attractive topologies in high-power medium-voltage motor drive applications due to its modularity and scalability. Research in high power regenerative motor drives has gained significant attention with the increasing demand for efficient energy use. In a cascaded H-Bridge (CHB) converter, the regenerative capability can be introduced by replacing diode front end (DFE) with active front end (AFE) topologies. However, this results in a huge increase in the number of power semiconductors, gate drivers, and heat sink size and thus increases the overall size and cost of the regenerative CHB motor drives. To overcome the aforementioned challenges, different power cell designs have been introduced to reduce the switch count, allowing the design of more suitable-sized and more economical drives. This paper comprehensively reviews the reduced switch-count power cell designs, including single-phase and three-phase grid connections. Each reduced switch-count cell design is analyzed, and its advantages and disadvantages are studied in detail. The challenges that arise with each design and the method to address the challenges are discussed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.667

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 head0.034
GPT teacher head0.285
Teacher spread0.251 · 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 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

Citations18
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueIEEE AccessSame topicMultilevel Inverters and ConvertersFrench-language works237,207