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Record W2120403691 · doi:10.1109/wescan.1995.494080

High-speed differential protection of parallel teed transmission lines

2002· article· en· W2120403691 on OpenAlexaff
Manoj Sachdev, T.S. Sidhu, X. Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTrippingElectric power transmissionComputer scienceElectric power systemProtective relayTransmission systemTransmission lineLine (geometry)Data transmissionTransmission (telecommunications)Power (physics)AlgorithmControl theory (sociology)Electronic engineeringCircuit breakerEngineeringElectrical engineeringComputer hardwareControl (management)MathematicsTelecommunications

Abstract

fetched live from OpenAlex

This paper presents high-speed differential protection algorithm for parallel-teed transmission lines. The algorithm uses distributed parameter models of the transmission lines and synchronized sampling of data at the terminals of the line. A method that takes the unsynchronised data and generates synchronised information is described. The proposed algorithm uses a segregated scheme, facilitates the coordination of multiphase tripping and reclosing on HV and EHV parallel-teed transmission lines. A relaying system design based on the proposed algorithm is also discussed The proposed system was tested extensively by using data that was generated by using the EMTDC and sample power systems. Results show that the system remains stable during normal operation of the power system and during external faults. The relaying system is sensitive and fast and does not jeopardize security. Some test results are included in the paper.

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.609
Threshold uncertainty score0.531

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 head0.015
GPT teacher head0.197
Teacher spread0.181 · 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 teacher head, 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

Citations2
Published2002
Admission routes1
Has abstractyes

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