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Record W3158455171 · doi:10.1109/tie.2021.3076727

DC-Link Voltage Ripple Control of Regenerative CHB Drives for Capacitance Reduction

2021· article· en· W3158455171 on OpenAlexafffund
Zhituo Ni, Ahmed Abuelnaga, Mehdi Narimani, Navid R. Zargari

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsRockwell Automation (Canada)McMaster University
FundersNatural Sciences and Engineering Research Council of CanadaOntario Centres of Excellence
KeywordsRippleCapacitorCapacitanceControl theory (sociology)Rectifier (neural networks)Controller (irrigation)VoltageComputer scienceElectronic engineeringEngineeringElectrical engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

The diode-front-end cascaded H-bridge (CHB) inverters have prevailed in the nonregenerative industry drive domain for high-power medium-voltage applications. The regenerative version of the CHB drives is made possible by adding the extra active-front-end rectifier in each power cell, such as a three-phase PWM rectifier. However, due to the instantaneous power unbalance, the dc-link capacitors of the regenerative power cell need to be overdesigned to maintain a stable low ripple dc-link voltage. To reduce the dc-link capacitance, this article proposes a novel closed-loop voltage ripple controller for the regenerative CHB drive without adding extra sensors. In the proposed method, dc-link voltage ripple amplitude and phase angle are accurately detected with a high-performance adaptive filter. Moreover, a latent instability issue is discussed and is avoided in the proposed controller. The performance of the proposed control strategy 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.879

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 head0.025
GPT teacher head0.224
Teacher spread0.200 · 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
Published2021
Admission routes2
Has abstractyes

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