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Record W1989156586 · doi:10.1109/tpwrd.2013.2294651

Enhanced Equivalent Model of the Modular Multilevel Converter

2014· article· en· W1989156586 on OpenAlexaff
Firouz Badrkhani Ajaei, Reza Iravani

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2014
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsModular designFault (geology)Representation (politics)Equivalent circuitTopology (electrical circuits)Control theory (sociology)Electronic engineeringEngineeringComputer scienceVoltageElectrical engineering

Abstract

fetched live from OpenAlex

This paper introduces an enhanced equivalent model of the half-bridge symmetrical monopole modular multilevel converter (MMC) topology for the analyses of the electromagnetic transients. Compared with the existing MMC models, the proposed model reduces the simulation computational time without compromising accuracy, and contrary to the existing models, it provides accurate representation of the MMC behavior with respect to the dc-side fault. This paper also evaluates and verifies the accuracy of the proposed equivalent model under various operating conditions, that is, startup, dc-side pole-to-pole fault, and ac-side three-phase-to-ground fault. The study results indicate that: 1) in contrast to the existing equivalent models, the proposed enhanced model accurately represents the MMC under various operating conditions; 2) the implemented simplifications to the model do not adversely affect the accuracy of the simulation results; and 3) a reduced-level approximated model of the MMC is also adequately accurate for protection studies.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.198
Teacher spread0.184 · 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

Citations97
Published2014
Admission routes1
Has abstractyes

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