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Record W2099170502 · doi:10.1109/tpwrd.2010.2041373

Detection of Incipient Faults in Distribution Underground Cables

2010· article· en· W2099170502 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2010
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsWestern University
Fundersnot available
KeywordsFault (geology)Fault indicatorEngineeringFault detection and isolationTime domainWaveletVoltageComputer scienceElectrical engineeringGeologySeismologyArtificial intelligence

Abstract

fetched live from OpenAlex

The incipient faults in underground cables are largely caused by voids in cable insulations or defects in splices or other accessories. This type of fault would repeatedly occur and subsequently develop to a permanent fault sooner or later after its first occurrence. Two algorithms are presented to detect and classify the incipient faults in underground cables at the distribution voltage levels. Based on the methodology of wavelet analysis, one algorithm is to detect the fault-induced transients, and therefore identify the incipient faults. Based on the analysis of the superimposed fault current and negative sequence current in the time domain, the other algorithm is particularly suitable to detect the single-line-to-ground (SLG) incipient faults, which are mostly occurring in underground cables. Both methods are designed to be applied in real systems. Hence, to verify the effectiveness and functionalities of the proposed schemes, different fault conditions, various system configurations, and real field cases are examined and other transients caused by permanent fault, capacitor switching, load changing, etc., are studied as well.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.467

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 head0.220
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