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Record W1919546079 · doi:10.1109/pes.2003.1267446

Using impedance measurement to improve the reliability of traveling-wave distance protection

2004· article· en· W1919546079 on OpenAlexafffund
V. Pathirana, E. Dirks, P.G. McLaren

Bibliographic record

Venue2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491) · 2004
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaManitoba Hydro
KeywordsRelayFault (geology)Electrical impedanceTransient (computer programming)Reliability (semiconductor)Electric power transmissionTransmission lineElectronic engineeringProtective relayPower-system protectionElectric power systemElectrical engineeringDigital protective relaySIGNAL (programming language)Computer scienceEngineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

Two common problems associated with distance protection schemes based on traveling waves are close-up faults and faults with small inception angles. This paper examines how the reliability of such a relay may be improved by considering the measured impedance at the relay location. The composite algorithm describes how the traveling wave information and line impedance measurement can be used in parallel in a single protection scheme to develop a high-speed, reliable distance relay. Such a scheme will be extremely useful for long EHV transmission lines where high speed fault clearance is essential. The effects of close-up faults, fault inception angle, and external faults on a three-phase 500 kV power system are examined using an electromagnetic transient program. A laboratory prototype of the relay has been developed using a high speed signal processor.

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)0.009
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.222
Teacher spread0.201 · 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

Citations1
Published2004
Admission routes2
Has abstractyes

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