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Record W2125404375 · doi:10.1109/pess.2001.970002

Wide-band 138 kV distributed-sensor optical voltage transducer: study of accuracy under pollution and other field disturbances

2001· article· en· W2125404375 on OpenAlexaff
F. Rahmatian, P.P. Chavez, Nicolas A. F. Jaeger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagneto-Optical Properties and Applications
Canadian institutionsUniversity of British ColumbiaAXYS Technologies (Canada)
Fundersnot available
KeywordsPartial dischargeShielded cableTransducerVoltageInsulator (electricity)Electrical engineeringHigh voltageImpulse (physics)Materials scienceElectric fieldElectrical conductorAcousticsEnvironmental scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

A 138 kV optical voltage transducer (VT) using shielded distributed electric field sensors is presented for use in high-voltage (HV) electric power transmission systems. Since HV and ground are kept far apart, the VT does not require oil or SF/sub 6/ for insulation; instead, it uses the environmentally friendly dry nitrogen as an insulating gas. A prototype has been tested for accuracy under various severe field disturbances including the presence of conductive pollution-like layers on the insulator and the presence of other HV sources nearby. The test results show that the VT meets IEC 0.2% and IEEE 0.3% accuracy classes under these extreme field disturbances. Data that demonstrates wide bandwidth of the VT are also presented. Another prototype has been tested and has successfully passed standard IEC HV dielectric withstand tests including power frequency withstand at 275 kV, partial discharge <5 pC, lightning impulse tests (BIL) at /spl plusmn/650 kV, and chopped impulse tests at -750 kV.

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)0.002
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: 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
Metaresearch0.0000.002
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 integrity0.0010.000
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 head0.017
GPT teacher head0.236
Teacher spread0.219 · 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

Citations7
Published2001
Admission routes1
Has abstractyes

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