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Record W2604918715 · doi:10.1109/tpwrd.2017.2691343

Frequency-Dependent Multiconductor Transmission Line Model With Collocated Voltage and Current Propagation

2017· article· en· W2604918715 on OpenAlexaff
José R. Martí, Arash Tavighi

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEmtpTransmission lineFrequency domainTelegrapher's equationsTransformation (genetics)Mathematical analysisTime domainConstant (computer programming)Transformation matrixMatrix (chemical analysis)Line (geometry)VoltagePhysicsMathematicsComputer scienceGeometryClassical mechanicsTelecommunicationsElectric power system

Abstract

fetched live from OpenAlex

This paper reviews the classical multiconductor transmission line (MTL) equations and proposes additional constraints on these equations. A fundamental physical constraint is that the voltage and current waves must be collocated and travel together with the same propagation function. Based on this condition, the Revised Multiconductor Transmission Line (RMTL) equations are proposed. As opposed to the classical MTL equations that require complex frequency-dependent transformation matrices for their diagonalization, the RMTL equations can be diagonalized very accurately using a single real constant transformation matrix. A new Frequency-Dependent Line Model (FDLM) is proposed based on the RMTL equations. FDLM is compared with the two most accepted frequency-dependent line models in the Electromagnetic Transients Program (EMTP): The JMARTI model (fdLine) that uses a constant transformation matrix as an approximation, and the phase-coordinates Universal Line Model (ULM) that fits the frequency dependence of the transformation matrices. These time-domain models are compared with a reference frequency-domain solution for a double-circuit vertical line.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.657

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 studies0.0010.000
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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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 designBench or experimental
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

Citations36
Published2017
Admission routes1
Has abstractyes

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