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Record W2118243046 · doi:10.1109/pes.2006.1708888

Robust non-communication line protection scheme using novel quantities

2006· article· en· W2118243046 on OpenAlexaff
H.A. Darwish, Tamer A. Kawady, A.-M.I. Taalab, O.P. Malik

Bibliographic record

Venue2006 IEEE Power Engineering Society General Meeting · 2006
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTrippingScheme (mathematics)Computer scienceModalCircuit breakerTransmission lineFault (geology)Line (geometry)Electronic engineeringFuse (electrical)Transmission (telecommunications)EngineeringTelecommunicationsElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

A robust non-communication scheme for transmission line protection is presented. The scheme facilitates the simultaneous detection of the remote breaker operation in order to adaptively accelerate zone 2 tripping of the local zone. A modal transform-based decision algorithm is proposed to efficiently detect symmetrical/unsymmetrical faults. Modal quantities as well as current phase angle variations are monitored during and post-fault, and translated into hypothetical communication flags. Therefore, the benefits of modern communication-based schemes can be approached without utilizing physical communication links. A simulation study is carried out to investigate the scheme performance. Test results corroborate the high performance of the proposed adaptive scheme for variable faults

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.215
Teacher spread0.194 · 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 designSimulation or modeling
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

Citations8
Published2006
Admission routes1
Has abstractyes

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