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Record W2117135540 · doi:10.1109/61.871353

Transmission line arrester energy, cost, and risk of failure analysis for partially shielded transmission lines

2000· article· en· W2117135540 on OpenAlexaff
E. Tarasiewicz, F. Rimmer, A.S. Morched

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsHydro One (Canada)
Fundersnot available
KeywordsSurge arresterShielded cableLightning (connector)Lightning arresterElectric power transmissionEngineeringTransmission lineElectrical engineeringElectromagnetic shieldingLine (geometry)Reliability engineeringElectrical conductorPower (physics)Physics

Abstract

fetched live from OpenAlex

Application of metal oxide surge arresters in power systems has been traditionally linked to electrical equipment protection. The industry has noted a very significant increase in the application of metal oxide arresters on transmission lines in an effort to reduce lightning initiated flashovers. This paper describes a cost-effective installation of surge arresters on a partially shielded transmission line. It compares several options with respect to cost and gains in terms of lightning performance improvement. This paper also presents a new approach to calculate risk of failure of transmission line surge arresters (TLSAs) due to lightning strokes to towers, shield/phase conductors, and illustrates the method with an example for a partially shielded line. Results show that the risk of failure is very small. The installation of TLSAs completed in 1997 demonstrated that it is possible to afford adequate lightning protection levels by selectively applying surge arresters only to the towers most sensitive to backflashover and shielding failures.

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)0.002
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.216
Teacher spread0.209 · 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

Citations50
Published2000
Admission routes1
Has abstractyes

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