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Current Harmonic Reduction in DC-Link Capacitors of a Regenerative Cascaded H-Bridge Converter

2020· article· en· W3095823975 on OpenAlexaff
Shaoyi Yuan, Mehdi Narimani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCapacitorRippleHarmonicsPulse-width modulationHarmonicHarmonic analysisElectrical engineeringElectronic engineeringCurrent (fluid)Computer scienceTopology (electrical circuits)Control theory (sociology)PhysicsEngineeringVoltageAcoustics

Abstract

fetched live from OpenAlex

This paper proposes a new approach to reduce current harmonics in the power cell dc-link capacitors of a regenerative cascaded H-bridge (regen-CHB) converter. In order to suppress the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> carrier band dominant harmonic current injected into dc-link capacitor, a pulse width modulation (PWM) carrier shifted angle optimization is proposed. This proposed method is based on an optimization ideology, which can promote dc-link capacitor's performance on ripple current rating and power loss. The theoretical analysis of dc-link capacitor current harmonics and corresponding optimization ideology for the problem is studied in this paper. Compared to the other existing methods for dc-link capacitor current ripple suppression, the proposed method requires no online parameter update and realtime computation. The simulation results show a noticeable elimination of dominant current harmonics which reduces rms current and current ripples of the dc-link capacitors significantly.

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 score0.777
Threshold uncertainty score0.595

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 head0.045
GPT teacher head0.240
Teacher spread0.196 · 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
Published2020
Admission routes1
Has abstractyes

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