MétaCan
Menu
Back to cohort
Record W3160060689 · doi:10.1109/tpwrd.2021.3078767

Investigation on the Implementation of the Single-Sheath Bonding Method for Power Cables

2021· article· en· W3160060689 on OpenAlexafffund
Xi Wang, Jing Yong, Lulu Li

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2021
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsAmpacityWire bondingMaterials scienceAnodic bondingConductorElectrical conductorComputer scienceMechanical engineeringComposite materialEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Sheath bonding is an essential requirement for transmission and distribution cables. Three bonding methods are widely used in industry at present: solid bonding, single-point bonding, and cross bonding. The latter two are for solving the ampacity reduction problem caused by solid bonding. This paper presents an in-depth investigation of a new cable sheath bonding method called single-sheath bonding. The new method uses one of the cable sheaths as the return path for the fault current and thus eliminates the need for the ground continuity conductor (GCC). In this paper, various configurations to implement the single-sheath bonding method are proposed. Their performances are comprehensively evaluated and compared with those of the existing bonding methods through simulation and sensitivity studies. The results show that single-sheath bonding can achieve a similar performance of either single-point bonding or cross bonding with a lower cost. Therefore, single-sheath bonding can be an alternative to single-point bonding and cross bonding. The potential applications of the single-sheath bonding method are also clarified in the paper.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.029
GPT teacher head0.258
Teacher spread0.229 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Power DeliverySame topicThermal Analysis in Power TransmissionFrench-language works237,207