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Record W3029091375 · doi:10.1109/jestpe.2020.2998738

Modular Multilevel Series/Parallel Converter for Bipolar DC Distribution and Transmission

2020· article· en· W3029091375 on OpenAlexafffund
Ricardo Lizana, Sebastian Rivera, Zhongxi Li, Apparao Dekka, L. W. Rosenthal, Hans Bahamonde I., Angel V. Peterchev, Stefan M. Goetz

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsLakehead University
FundersFondo Nacional de Desarrollo Científico y TecnológicoComisión Nacional de Investigación Científica y TecnológicaDuke UniversityNatural Sciences and Engineering Research Council of CanadaNorth Carolina Biotechnology CenterNational Science Foundation
KeywordsModular designConvertersSeries and parallel circuitsScalabilityComputer scienceTransmission systemElectronic engineeringVoltageSeries (stratigraphy)GridForward converterElectrical engineeringTopology (electrical circuits)Transmission (telecommunications)EngineeringBoost converterTelecommunications

Abstract

fetched live from OpenAlex

This article proposes a novel bipolar-type dc system suitable for both distribution and transmission systems based on modular multilevel series/parallel converters (MMSPCs). The system features decoupled operations of each pole of the bipolar system, being able to operate in both asymmetrical and regenerative modes. This enables two independent dc systems by using a single grid-tied converter. The MMSPC is based on a three-switch cell configuration and enables a simple balancing mechanism in combination with a wide range of output voltage frequencies. The simple balancing mechanism is the key to enable the dc operation and lead to simpler scalability for different voltage levels. Theoretical studies and experimental results are provided to verify and characterize the proposed system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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)0.0040.001

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 head0.009
GPT teacher head0.223
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2020
Admission routes2
Has abstractyes

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Same venueIEEE Journal of Emerging and Selected Topics in Power ElectronicsSame topicHVDC Systems and Fault ProtectionFrench-language works237,207