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Record W1963819684 · doi:10.1109/epec.2011.6070246

Transient behavior of static Fault Current Limiter in distribution system

2011· article· en· W1963819684 on OpenAlexaff
Esmaeil Najafi, Vijay K. Sood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsFault current limiterCurrent limitingTransient (computer programming)LimiterFault (geology)EmtpComputer scienceElectric power systemLimit (mathematics)Power (physics)Current (fluid)Control theory (sociology)Electrical engineeringEngineeringTelecommunicationsControl (management)Physics

Abstract

fetched live from OpenAlex

Continuous growth of electrical energy demand and expansion of electric distribution systems has resulted in a corresponding increase in fault current levels in distribution systems. The protection of legacy equipment and continuous upgrading of feeders is both expensive and often impractical. Effective fault current limiting has thus become a critical factor. This problem may be partially solved by deploying Fault Current Limiters. This paper investigates the behavior of a Static Fault Current Limiter (SFCL) installed in a simple radial distribution grid simulated by Electro Magnetic Transient Program (EMTP). In order to integrate the SFCL into power grids, and because one of the problems concerning power semiconductor current limiters is the stress imposed by transients and the impact on sensitive loads; it is required to predict the behavior of the switching transients when fault occurs and switching actions take place. The results demonstrate how the SFCL can rapidly limit the fault current to an acceptable limited value. Simultaneously controlled turn OFF signals are initiated by the controller in order to mitigate the possible stresses produced by switching actions. Though, some of the disadvantages of using a SFCL are the cost and continuous power loss.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.341

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)0.0000.000

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 head0.022
GPT teacher head0.225
Teacher spread0.203 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2011
Admission routes1
Has abstractyes

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