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Record W4384519045 · doi:10.1109/tec.2023.3293488

Constant-Parameter Voltage-Behind-Reactance Synchronous Machine Models Considering Main Flux Saturation for EMTP-Type Programs

2023· article· en· W4384519045 on OpenAlexafffund
Erfan Mostajeran, Navid Amiri, Juri Jatskevich

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2023
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReactanceEmtpInterfacingControl theory (sociology)Computer scienceVoltageConstant (computer programming)Transient (computer programming)Electronic engineeringControl engineeringEngineeringElectrical engineeringElectric power systemPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The state-of-the-art saturable phase-domain (PD) and voltage-behind-reactance (VBR) synchronous machine models have been recently considered in the literature as reliable and accurate alternatives to the traditionalqd0 models. However, due to the rotor-position- and saturation-dependent interfacing circuit, implementing these models in electromagnetic transient (EMT) simulators has been challenging due to the need to refactorize the machine-network conductance matrix at each time step during simulation. This article presents two new VBR-based synchronous machine models with main flux saturation that yield constant interfacing conductance matrix in EMTP-type solution. Case studies in PSCAD/EMTDC verify that the proposed models demonstrate numerical accuracy similar to the state-of-the-art models, while achieving significant computational gains in multimachine systems.

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)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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)0.0030.001

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 head0.020
GPT teacher head0.219
Teacher spread0.198 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Energy ConversionSame topicHVDC Systems and Fault ProtectionFrench-language works237,207