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Record W4366832292 · doi:10.1049/tje2.12267

Adaptive overcurrent relay (AOCR) based on Fano Factors of current signals

2023· article· en· W4366832292 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOvercurrentRelayDigital protective relayProtective relayPhasorComputer scienceControl theory (sociology)Electronic engineeringFault (geology)Electric power systemCurrent (fluid)EngineeringPower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

Abstract A Fano Factor‐based adaptive overcurrent protection scheme, that automatically adjusts the protection settings in response to the prevailing conditions of the power system, is presented. Both phase and ground overcurrent relays are based on the same mathematical algorithm to calculate the operating time using Fano Factors (FFs) estimated for phase and ground current signals, respectively. Effectiveness of the proposed adaptive overcurrent method is validated for various fault types under a plethora of loading current levels, besides a wide range of fault inception angles and fault resistance. Simulation results confirm that the proposed algorithm can detect series and shunt faults, differentiate between grounding and phase faults, and adapt the relay operating time settings using the FF for all conditions examined. The relay algorithm speed and sensitivity are controllable using both the FF setting values and moving data window size. Moreover, it is characterized by being simple, reliable, accurate, and can be implemented practically as a base of digital protective relay for use in substation automation systems and phasor measurement units.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.249
Teacher spread0.222 · 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