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Volt-time Flashover Characteristics and Lightning Performance of a Compact EHV Transmission Line with Composite Insulated Cross-arms

2023· article· en· W4389777073 on OpenAlexaff
Usama Ahmed, Yanlin Li, Xinlong Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsCanadian Red Cross Society
Fundersnot available
KeywordsArc flashTransmission lineLightning (connector)Electric power transmissionElectrical engineeringVoltageTransient (computer programming)Line (geometry)Composite numberVoltEngineeringMaterials sciencePhysicsComputer sciencePower (physics)Composite materialMathematicsGeometry

Abstract

fetched live from OpenAlex

Compact lines offer various technical advantages and can support energy transition in a resource conscious and environmentally friendly manner. This paper presents the Volt-time flashover tests and electromagnetic transient simulations performed to optimize the insulation design of a compact 345 kV transmission line with rotatable (pivoting type) composite insulated cross-arms. Two design variants of the insulated cross-arm were tested. The obtained Volt-time curves indicated that the design of the high voltage yoke of the insulated cross-arms has minimal influence on the lightning flashover characteristics. These curves also exhibited a close alignment with the curve predicted using the CIGRE leader progression model. Best-fit leader progression model constants were derived for the insulated cross-arms from their tested Volt-time curve and implemented in PSCAD. The critical flashover currents were estimated and the backflashover performance of the compact line was found be significantly improved in comparison to an equivalent non-compact traditional design.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.218
Teacher spread0.212 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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