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Record W2143562416 · doi:10.1109/pess.2001.970001

Interfacing optical current sensors in a substation

2001· article· en· W2143562416 on OpenAlexaff
J.D.P. Hrabliuk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagneto-Optical Properties and Applications
Canadian institutionsAXYS Technologies (Canada)
Fundersnot available
KeywordsInterfacingCurrent transformerComputer scienceTransformerKey (lock)Current sensorElectrical engineeringElectronic engineeringBandwidth (computing)Optical pathEngineeringVoltageTelecommunicationsComputer hardware

Abstract

fetched live from OpenAlex

Optical current sensors are achieving increased acceptance and use in high voltage substations due to their superior accuracy, bandwidth, dynamic range and inherent isolation. Once deemed specialized devices intended for novel applications, optical sensors have risen to a performance level exceeding conventional magnetic devices. Optical current sensors have pushed the limits of measurement beyond what is presently available with conventional technology. In order for users of optical transducers to realize the complete benefits available from this new technology, an understanding of the differences between conventional transformers and optical sensors is critical. Key users who must understand these differences include planners, apparatus engineers, substation designers, maintenance personnel and system operators. Interfacing optical sensors to existing meters and relays is one of these key differences and is the primary focus of this paper. Providing a simple method of interconnecting old and new technology is a necessary and required step in the path to acceptance of optical sensors, and if given this, it will ease the transition to advanced systems within a substation.

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

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 studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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
Published2001
Admission routes1
Has abstractyes

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