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Record W4382119267 · doi:10.1109/jestpe.2023.3289485

Voltage Ripple Model and Capacitor Sizing for the Three-Phase Four-Wire Converter Used for Power Redistribution

2023· article· en· W4382119267 on OpenAlexafffund
Isla Ziyat, Jiacheng Wang, Patrick Palmer

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2023
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsRippleCapacitorElectrical engineeringRedistribution (election)VoltageSwitched-mode power supplySizingMaterials scienceFilter capacitorVoltage doublerThree-phaseDecoupling capacitorBuck converterElectronic engineeringVoltage regulatorEngineeringDropout voltage

Abstract

fetched live from OpenAlex

Three-phase unbalance in distribution grids is becoming more time-varying and is set to increase due to the integration of single-phase distributed energy resources (DERs). Unbalance can be actively remediated using converters with a neutral connection to redistribute real and reactive power across all phases. This study considers the three-phase four-wire (3P-4W) converter, which can be used as a DER grid converter to provide grid services. By modeling the converter without a dc voltage source, it is ensured that power redistribution and reactive power compensation can take place even when the DER is not supplying power. Power redistribution is seen to create 60- and 120-Hz voltage ripples across each of the dc-link capacitors of the converter which result in undesired voltages on the ac side of the converter. Here, the precise time-domain capacitor voltage ripple is derived for each capacitor and experimentally verified. From this, a concise guide to capacitor sizing is provided to mitigate the undesired effects of power redistribution.

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: none
Teacher disagreement score0.927
Threshold uncertainty score0.593

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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

Explore more

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