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Record W2789776423 · doi:10.7716/aem.v7i1.613

Probabilistic Assessment of Lightning Related Risk of Transmission Lines Based on Frequency Dependent Modeling of Tower-Footing Grounding System

2018· article· en· W2789776423 on OpenAlexaff
J. Gholinejhad, Reza Shariatinasab, Keyhan Sheshyekani

Bibliographic record

VenueAdvanced Electromagnetics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsGroundTransmission towerElectric power transmissionEarthing systemTowerTransmission lineFrequency domainEngineeringLightning (connector)Moment (physics)Time domainEmtpProbabilistic logicStructural engineeringElectric power systemElectronic engineeringElectrical engineeringComputer scienceMathematicsMathematical analysisPhysicsStatisticsPower (physics)

Abstract

fetched live from OpenAlex

This paper presents a probabilistic evaluation, based on Monte-Carlo method, for the estimation of insulation risk of failure of overhead transmission lines (TLs). The proposed method takes into account the wide-band model of tower-footing grounding system. The wide-band model of grounding system in frequency domain is obtained by the method of moment solution to the governing electrical field integral equations. The electrical parameters of soil are considered to be either constant or frequency dependent. The time-domain representation of the grounding system is inferred through pole-zero characterization of its associated frequency response. The case of a typical 400-kV transmission line is modelled in EMTP_RV with the tower-footing grounding system integrated with the transmission line (TL) system. The results of the paper show that the failure risk of transmission lines is affected by the grounding system model. This effect is more pronounced when the soil electrical parameters are assumed to be frequency dependent.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.243
Teacher spread0.237 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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