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Record W3142953050 · doi:10.1109/mper.2002.4312231

A Robust Phase-Coordinates Frequency Dependent Underground Cable Model (Zcable) for the EMTP

2002· article· en· W3142953050 on OpenAlexaff
Tao Yu, José R. Martí

Bibliographic record

VenueIEEE Power Engineering Review · 2002
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEmtpTransformation (genetics)Frequency domainContext (archaeology)Control theory (sociology)Matrix (chemical analysis)Transient (computer programming)ModalTransformation matrixPhase (matter)Stability (learning theory)DiscretizationComputer scienceMathematicsMathematical analysisPhysics

Abstract

fetched live from OpenAlex

A major difficulty in multiphase cable modeling with traditional electromagnetic transient program like the EMTP is the synthesis of the frequency-dependent transformation matrix that relates modal and phase domain variables. This paper presents a new model (zCable) to represent the frequency-dependence of cable parameters directly in phase coordinates, thus avoiding the problems related to frequency-dependent transformation matrices. The cable model is split into two parts: a constant ideal line section and a frequency-dependent loss section. A pi-correction is proposed to solve the problem of different traveling times in the ideal line section. The main advantage of the proposed model as compared to existing frequency-dependent transformation matrix models is the model's absolute numerical stability for strongly asymmetrical cable configurations and for arbitrary fault conditions. The model parameters are easy to obtain with robust algorithms, and the model can be efficiently implemented in the context of a real-time PC-cluster simulator.

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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.039
GPT teacher head0.228
Teacher spread0.189 · 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
Published2002
Admission routes1
Has abstractyes

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