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Record W36287277

Performance enhancement of digital relays for transmission line distance protection

2003· dissertation· en· W36287277 on OpenAlexfundno aff
Feng Liang

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2003
Typedissertation
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of Newfoundland
KeywordsRelayDigital protective relayTransmission lineTransmission (telecommunications)AlgorithmProtective relayLine (geometry)Computer scienceFault (geology)Point (geometry)Electronic engineeringFilter (signal processing)Discrete Fourier transform (general)Fast Fourier transformSIGNAL (programming language)Electric power transmissionFourier transformEngineeringElectrical engineeringMathematicsTelecommunicationsShort-time Fourier transformFourier analysisComputer vision
DOInot available

Abstract

fetched live from OpenAlex

This thesis investigates the performance enhancement of digital relays for transmission line distance protection from filtering (extracting the fundamental frequency components) point of view and relaying algorithm point of view. -- Several Fourier based filtering algorithms are investigated, simulated, and compared in this thesis. Furthermore, a new wavelet transform based real-time filtering algorithm is proposed. The studies suggest that this method converges faster than Fourier based algorithms. -- Two types of relaying algorithms are also examined in this thesis. One algorithm, which compensates the fault resistance, only needs to monitor the voltage and current signal at the relay end. The other algorithm makes use of the information from both ends of the transmission line and adaptively adjusts the relay operating characteristics. Both theoretical analysis and simulation results show that these algorithms can significantly enhance the performance of digital relays for transmission line distance protection.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.019
GPT teacher head0.240
Teacher spread0.221 · 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.

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
Published2003
Admission routes1
Has abstractyes

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