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Record W1776078244 · doi:10.1109/wescan.1991.160545

Security testing of high impedance fault detectors

2002· article· en· W1776078244 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrical Fault Detection and Protection
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsElectrical impedanceHigh impedanceFault (geology)Arc (geometry)WaveformComputer scienceWeldingArc weldingArtificial intelligenceEngineeringElectrical engineeringVoltageMechanical engineeringSeismology

Abstract

fetched live from OpenAlex

The security of high-impedance fault (HIF) relays is examined. Waveforms of HIFs as well as loads that behave to appear like HIFs were collected and processed. The detection parameters used in a number of present HIF detection algorithms were extracted and compared to test the algorithms' ability to discriminate between faults and fault-like loads. It was found that there are loads that imitate HIFs, and that the present algorithms lack the security required under these load conditions. The high-impedance fault-like loads considered were: an arc welding machine; a personal computer; and a fluorescent light. It was found that arc welders can easily imitate HIF conditions, and that the tested algorithms showed insecurity when tested with an arc welder load.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.358

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 head0.014
GPT teacher head0.201
Teacher spread0.187 · 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