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Record W2014505733 · doi:10.1109/epec.2012.6474964

Effect of Static Fault Current Limiter on distribution power quality

2012· article· en· W2014505733 on OpenAlexaff
Esmaeil Najafi, Vijay K. Sood, Ahmed Hosny

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsVoltage sagFault current limiterFault (geology)EmtpVaristorRectifier (neural networks)Current limitingOvervoltageVoltageComputer sciencePower (physics)Insulated-gate bipolar transistorInductorElectrical engineeringElectric power systemElectronic engineeringEngineeringPower qualityPhysics

Abstract

fetched live from OpenAlex

High demand for sustainable electric energy makes the use of Distributed Generators (DGs) inevitable for future systems. However, introducing DGs into distribution networks increases fault current levels with potential for causing serious damage to power system apparatus. Increasingly, power quality during fault and recovery periods is also adversely affected. To alleviate these problems, a Static Fault Current Limiter (SFCL) is investigated in this work as a potential solution. The studied SFCL comprises a bridge rectifier with semiconductor switch IGBT bypassed by limiting inductor and ZnO Varistor. A case study of a 15-kV radial distribution system with SFCL located at the source end is simulated using EMTP-RV software. Simulation results following system faults show that the use of SFCL in distribution networks can efficiently suppress fault current magnitudes and enhance the power quality in terms of voltage sag.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.302
Teacher spread0.290 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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