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Record W2140562988 · doi:10.1109/tpwrd.2008.915778

Restoration of Directional Overcurrent Relay Coordination in Distributed Generation Systems Utilizing Fault Current Limiter

2008· article· en· W2140562988 on OpenAlexaff
Walid El‐Khattam, T.S. Sidhu

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2008
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsWestern University
Fundersnot available
KeywordsOvercurrentRelayFault current limiterFault (geology)Protective relayPower-system protectionLimiterDigital protective relayElectric power systemDistributed generationEngineeringInstallationElectrical impedanceResistive touchscreenElectrical engineeringPower (physics)Computer scienceElectronic engineeringCurrent (fluid)Physics

Abstract

fetched live from OpenAlex

A new approach is proposed to solve the directional overcurrent relay coordination problem, which arises from installing distributed generation (DG) in looped power delivery systems (PDS). This approach involves the implementation of a fault current limiter (FCL) to locally limit the DG fault current, and thus restore the original relay coordination. The proposed restoration approach is carried out without altering the original relay settings or disconnecting DGs from PDSs during fault. Therefore, it is applicable to both the current practice of disconnecting DGs from PDSs, and the emergent trend of keeping DGs in PDSs during fault. The process of selecting FCL impedance type (inductive or resistive) and its minimum value is illustrated. Three scenarios are discussed: no DG, the implementation of DG with FCL and without FCL. Various simulations are carried out for both single- and multi-DG existence, and different DG and fault locations. The obtained results are reported and discussed.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.239
Teacher spread0.207 · 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 designSimulation or modeling
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

Citations284
Published2008
Admission routes1
Has abstractyes

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