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Record W3157209029 · doi:10.1109/tpwrs.2021.3077391

Hybrid Average-Value/Detailed Modeling of Line-Commutated AC–DC Converters With Internal Faults For Electromagnetic Transient Simulations

2021· article· en· W3157209029 on OpenAlexafffund
Seyyedmilad Ebrahimi, Navid Amiri, Juri Jatskevich

Bibliographic record

VenueIEEE Transactions on Power Systems · 2021
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransient (computer programming)ConvertersComputer sciencePower (physics)Line (geometry)Hybrid systemElectric power systemControl theory (sociology)Electronic engineeringVoltageEngineeringElectrical engineeringMathematicsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Recently, a hybrid average-value/detailed modeling technique has been presented for efficient electromagnetic transient (EMT) simulation of power systems containing ac-dc line-commutated rectifiers (LCRs). In this letter, the hybrid modeling methodology is extended to consider asymmetrical operation of LCRs due to internal faults of switches. The hybrid model permits large time-steps as opposed to conventional switching models that require precise handling of switching events and respectively small time-steps. The extended hybrid model is also demonstrated to possess numerical advantages over the existing state-of-the-art average-value models (AVMs) of faulty LCRs while providing more accurate 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 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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)0.0020.000

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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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