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Record W2920388989 · doi:10.3390/app9050930

Wet Snow Flashover Characteristics of 500-kV AC Insulator Strings with Different Arrangements

2019· article· en· W2920388989 on OpenAlexaff
Jingwei Xu, Fanghui Yin, Longji Li, Qingfeng Wen, Hao Wang, Shunnan Liu, Zhidong Jia, M. Farzaneh

Bibliographic record

VenueApplied Sciences · 2019
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsUniversité du Québec à Chicoutimi
FundersChina Electric Power Research InstituteNational Natural Science Foundation of ChinaElectric Power Research Institute
KeywordsArc flashSnowInsulator (electricity)IcingMaterials scienceElectrical engineeringEnvironmental scienceComposite materialForensic engineeringEngineeringMeteorologyPhysics

Abstract

fetched live from OpenAlex

In order to study the wet snow flashover characteristics of 500-kV AC insulator strings under different arrangements, wet snow flashover tests were carried out in the large climate chamber of China Electric Power Research Institute (CEPRI). The wet snow flashover voltages were obtained by the even-rising method and the flashovers were filmed by a camera. The test results showed that the installation of an anti-icing shed of large diameter could increase the wet snow flashover voltage. The distance between the two insulators was a key parameter that influenced the discharge process and the flashover voltage. Under Λ-string arrangement, for common insulators, the flashover performance of iced insulators increased with the connection angle; for anti-icing insulators, the flashover performance increased at first and then decreased with the connection angle. In wet snow conditions, when the connection angle was at the commonly adopted angle of 60°, the flashover performance of the common insulators under the V-string and Λ-string arrangements was almost the same. For anti-icing insulators, however, the V-string arrangement was recommended according to the tests. The results obtained in this study can provide a reference for external insulation design in wet snow conditions.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.368

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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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 designObservational
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

Citations13
Published2019
Admission routes1
Has abstractyes

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