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Record W2120843541 · doi:10.1109/tpwrs.2009.2032659

Including Magnetic Saturation in Voltage-Behind-Reactance Induction Machine Model for EMTP-Type Solution

2009· article· en· W2120843541 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEmtpReactanceVariable bitrateControl theory (sociology)Nonlinear systemSaturation (graph theory)Computer scienceVoltageMathematicsEngineeringElectric power systemPhysicsElectrical engineeringArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

A voltage-behind-reactance (VBR) machine model has been recently proposed for the electro-magnetic transient programs (EMTP)-type simulation programs. The VBR model greatly improves numerical accuracy and efficiency compared with the traditional <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">qd</i> and phase-domain (PD) models. This paper extends the previous research and presents an approach to include magnetic saturation into the VBR induction machine model. The presented method takes into account the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">qd</i> axes static and dynamic cross saturation, whereas the nonlinear magnetic characteristic is represented using a piecewise-linear method that is suitable for the EMTP solution approach. Case studies verify the new saturable VBR model and show that it has improved numerical stability and accuracy even at large time steps.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.652
Threshold uncertainty score1.000

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 head0.032
GPT teacher head0.253
Teacher spread0.221 · 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