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Record W2129942545 · doi:10.1109/tpwrs.2004.836165

Transient Analysis of Systems With Multiple Nonlinear Elements Using TLM

2004· article· en· W2129942545 on OpenAlexaff
Venkata Dinavahi

Bibliographic record

VenueIEEE Transactions on Power Systems · 2004
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNonlinear systemTransient (computer programming)Convergence (economics)Transmission lineTransient analysisNewton's methodElectric power systemComputer scienceElectric power transmissionTransmission systemControl theory (sociology)Transient responseAlgorithmTransmission (telecommunications)Mathematical optimizationMathematicsPower (physics)EngineeringPhysicsTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

This letter compares the performance of conventional Newton-Raphson (N-R) algorithm with that of the transmission line modeling (TLM) method for the simulation of systems containing multiple nonlinear elements. It is shown that the TLM method offers significant advantages in terms of convergence, accuracy and computational speed.

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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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 teacher head, not a consensus.

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
Published2004
Admission routes1
Has abstractyes

Explore more

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