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Record W2769985640 · doi:10.1109/ecce.2017.8095870

DC current balance with common-mode voltage reduction for parallel current source converters

2017· article· en· W2769985640 on OpenAlexaff
Li Ding, Yunwei Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRectifier (neural networks)Common-mode signalHarmonicsCurrent sourceConvertersNetwork topologyComputer scienceTopology (electrical circuits)Voltage sourceVoltageCurrent (fluid)Electrical engineeringPower (physics)Mesh analysisElectronic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Current source converter (CSC) is mainly applied in high medium voltage drives. To increase the system power region and reduce the output harmonics, parallel connection is an easy-implement and practical method. There are mainly two types of parallel based topologies. The direct parallel connect of two back-to-back CSCs is the first type, the two DC links can be controlled from the rectifier side independently. The second type constructed by one current source rectifier (CSR) on the grid side and two parallel current source inverters (CSIs) on the motor side, which can reduce the size and cost of the system. With two inverters sharing the same DC link, the DC current should be guaranteed with proper modulation to make the system work properly. Moreover, common-mode voltage (CMV) increases the line-to-ground voltage and damage the insulation system, which should also be suppressed. In this paper, the two types of topologies are compared from the DC current balance, CMV and circulating current point of views. An optimized multilevel based DC current balance method considering the CMV reduction is applied in the second type of topology. The effectiveness of proposed method is verified with simulation and experimental results.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.893

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 head0.024
GPT teacher head0.274
Teacher spread0.250 · 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 designOther design
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

Citations6
Published2017
Admission routes1
Has abstractyes

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