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Record W4292603972 · doi:10.37798/2011601-4256

Reduction of the double-circuit flashovers on a 400 kV overhead line

2022· article· en· W4292603972 on OpenAlexaff
A. Xemard, J. Michaud, F. Maciela, T. Lassaigne, F. Sauvegrain, P. Auriol, J.G. Roumy, O. Saad, Q. Bui Van, A. Dutil

Bibliographic record

VenueJournal of Energy - Energija · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsElectrical engineeringArc flashEngineeringLightning (connector)Surge arresterLine (geometry)Overhead lineEmtpInsulator (electricity)OvervoltageLightning arresterVoltageElectric power systemPhysicsPower (physics)Mathematics

Abstract

fetched live from OpenAlex

Double circuit flashovers may cause very severe system disturbances when taking place on some critical double-circuit lines of an electrical network. Line arresters offer an efficient solution to protect these specific lines against double circuit outages due to lightning. This paper will study, on a double-circuit 400-kV line, the protection provided by line arresters against double circuit outages due to lightning. The efficiency of several configurations of line arresters will be compared. For that purpose, the double-circuit lightning flashover rates of the line with and without line arresters will be calculated using a newly developed software which includes a three-dimensional electro-geometric model and is able to take into account the random nature of lightning. This software automatically launches EMTP-RV (restructured version of EMTP) for analyzing fast front overvoltages impressed on line insulation. The energy stressing the line arresters will also be calculated in order to evaluate the risk of failure of the line arresters due to excess energy absorption. Furthermore, the effects of several other parameters such as the tower footing resistances, the lightning withstand voltages of insulator strings as well as the protective levels of line arresters will also be investigated.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.546

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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