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Record W1985607615 · doi:10.1109/pes.2006.1709543

A hybrid algorithm for high speed transmission line protection

2006· article· en· W1985607615 on OpenAlexaff
V. Pathirana, P.G. McLaren

Bibliographic record

Venue2006 IEEE Power Engineering Society General Meeting · 2006
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsTeshmont (Canada)
Fundersnot available
KeywordsTransient (computer programming)Reliability (semiconductor)RelayTransmission lineFault (geology)Computer scienceFault detection and isolationElectrical impedanceLimit (mathematics)Protective relayPower-system protectionLine (geometry)Electric power transmissionHigh impedanceTransmission (telecommunications)Electronic engineeringAlgorithmReliability engineeringEngineeringElectrical engineeringTelecommunicationsElectric power systemMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Summary form only given. A common method used in UHV transmission line protection is based on impedance measurement technique. The fault detection speed of impedance protection schemes cannot be improved without sacrificing the relay reach limit. Protection algorithms based on fault generated transient signals have shown promising results in improving the speed, but these methods have inherent reliability problems. This paper examines how the fault generated transient information can be used to achieve fast fault detection speeds in a distance protection scheme while maintaining a high reliability level

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)0.001
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: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.003

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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