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Record W4396506421 · doi:10.1109/tec.2024.3395183

Constant-Parameter Phase-Domain Synchronous Machine Modeling Considering Main Flux Saturation for EMTP-Type Solution

2024· article· en· W4396506421 on OpenAlexafffund
Erfan Mostajeran, Navid Amiri, Juri Jatskevich

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2024
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmtpSaturation (graph theory)Control theory (sociology)Constant (computer programming)Flux (metallurgy)Time constantSynchronous motorType (biology)Computer sciencePhysicsEngineeringMathematicsMaterials scienceElectric power systemGeologyElectrical engineeringThermodynamics

Abstract

fetched live from OpenAlex

The recently proposed state-of-the-art saturable voltage-behind-reactance (VBR) and phase-domain (PD) synchronous machine models offer improved numerical accuracy and stability compared to the classicalqd0 models in electromagnetic transient (EMT or EMTP)-type solution. However, these new models are interfaced with the external network through a variable-parameter (i.e., both rotor-position- and saturation-segment-dependent) interfacing circuit, necessitating refactorizing the entire system conductance matrix at each time step and hindering computational efficiency. This paper extends the previous work by proposing new saturable constant-parameter phase-domain (CPPD) synchronous machine models possessing constant interfacing circuits. The proposed CPPD models are demonstrated in MATLAB and PSCAD/EMTDC and have numerical accuracy similar to other state-of-the-art models while providing considerable computational savings.

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 score0.004
Threshold uncertainty score0.012

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 communication0.0000.001
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 head0.014
GPT teacher head0.226
Teacher spread0.211 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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