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Record W3126734372 · doi:10.1109/ias44978.2020.9334879

Algorithm to Prevent Breaker-Failure Protection Mal-operation Due to Subsidence Current

2020· article· en· W3126734372 on OpenAlexaff
Soumitri Jena, Bhavesh R. Bhalja, O.P. Malik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCircuit breakerCurrent transformerRelayProtective relayComputer scienceTransformerFault (geology)OvercurrentReliability engineeringControl theory (sociology)Electrical engineeringEngineeringCurrent (fluid)VoltageGeologyPower (physics)Physics

Abstract

fetched live from OpenAlex

Duplication of circuit breakers (CBs) in substations is not realistic due to economic and operational constraints. In the event of failure of a breaker, adjacent breakers need to be called in to isolate the fault. Typically, a breaker-failure protection (BFP) function is integrated within commercial relays to monitor such situations. However, these functions are prone to mal-operation because of subsidence in current transformers (CTs) resulting from faults with significant decaying DC component. To this end, a reliable reset algorithm for the BFP function is presented in this paper. After measuring the decaying DC component within a one-cycle moving window, the proposed algorithm accurately distinguishes between the alternating pattern of fault current and exponential decaying pattern in subsidence current. Its performance remains unaffected during change in fault current level, decaying dc component, CT saturation and level of subsidence. Results from simulation as well as laboratory setup indicate that the proposed algorithm is able to prevent the mal-operation of an existing BFP scheme in the conventional relay with safety margin in the range of 20-40%. A comparative evaluation with available techniques testifies its superiority.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.999

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)0.0000.001

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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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 designOther design
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
Published2020
Admission routes1
Has abstractyes

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