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Record W2108771312 · doi:10.1109/pes.2009.5275252

Method to include lumped devices in multi-conductor transmission line system models

2009· article· en· W2108771312 on OpenAlexaff
Ziya Mazloom, Nelson Theethayi, Rajeev Thottappillil

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsTransformerTransmission lineEmtpElectric power transmissionEngineeringElectronic engineeringTime domainCircuit breakerTransmission systemElectrical engineeringTransient (computer programming)Electric power systemGroundTraction power networkComputer scienceVoltageTransmission (telecommunications)Power (physics)

Abstract

fetched live from OpenAlex

Transient analysis and crosstalk in multiconductor transmission line (MTL) systems with internal and external losses has been widely studied by solving telegrapher's equation using the finite difference time domain (FDTD) method. Most of those studies are carried out with lumped loads/devices connected at the line terminations (at source and load ends). However, in practical systems such as typical railway and power systems, lumped devices, like, transformers, rotary converter stations, line interconnections and insulators, substation and switchgear equipments (grounding systems, insulators, surge protectors, circuit breakers, etc.) and signaling equipments exist either in series or in shunt with the MTL systems, but not necessarily at the ends. Detailed/complex circuit models (linear or nonlinear) for all those devices either exist in the literature or can be developed from experiments. In this paper a more general method based on Kirchoff's current law (nodal analysis) is proposed to interface the FDTD code with Alternative Transients Program/Electromagnetic Transients Program (ATP/EMTP) circuit simulation software for transient analysis with any complex circuit model along the MTL system. The method is efficient and simple and it could be beneficial for accurate and realistic transient protection, mitigation and insulation coordination studies in large distributed electrical systems. A case study is made for an electrified railway with booster transformer traction system.

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 categoriesnone
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 score0.760
Threshold uncertainty score0.494

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 head0.030
GPT teacher head0.305
Teacher spread0.274 · 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.

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

Citations5
Published2009
Admission routes1
Has abstractyes

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