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Record W4376851462 · doi:10.1109/tpwrd.2023.3275445

Dual-Arm Modular Multilevel Converter With a Compact Footprint for VSC-HVDC Applications

2023· article· en· W4376851462 on OpenAlexafffund
Levi Bieber, Liwei Wang, Juri Jatskevich

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2023
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModular designCapacitorElectrical engineeringCapacitive sensingEngineeringVoltageElectronic engineeringFault (geology)Energy storageConvertersComputer scienceTopology (electrical circuits)Power (physics)Physics

Abstract

fetched live from OpenAlex

In this paper, a novel dual-arm modular multilevel converter (DAC) with inherent DC-fault-blocking capability is proposed that needs only two stacks of submodules (SMs), as well as director switches (DSs), for multilevel AC/DC conversion. Compared to the state-of-the-art modular multilevel converter (MMC) technologies with six SM-based arms, the DAC's dual multiplexed converter arm structure, combined with needing approximately 75% less capacitive energy storage than the MMC, facilitate a smaller converter station footprint. This paper presents the operating principle of the DAC and benchmarks it against other voltage source converter technologies based on converter functionalities, component count, capacitive energy storage requirements, and semiconductor losses for high voltage direct current transmission (HVDC) application. For a wider output voltage range, overlap operation is proposed to enable circulating currents to balance the SM capacitors’ energy cycle-to-cycle. A 600 kV HVDC system simulation study is presented that demonstrates the DAC's ability to (1) transmit power bidirectionally under nominal conditions, and (2) extinguish fault currents during DC-side short-circuit fault. A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${200}\; {{{V}}}_{\text{DC}}$</tex-math></inline-formula> scaled-down converter hardware implementation further verifies the DAC's operating principle.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.853

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 head0.017
GPT teacher head0.226
Teacher spread0.209 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

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