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Record W2084874204 · doi:10.1109/tpwrd.2013.2276948

A Novel Fault Current Control Scheme to Reduce Synchronous DG's Impact on Protection Coordination

2013· article· en· W2084874204 on OpenAlexaff
Hesam Yazdanpanahi, Wilsun Xu, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2013
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOvercurrentFault (geology)Transient (computer programming)Power-system protectionFault current limiterResidual-current deviceGenerator (circuit theory)Permanent magnet synchronous generatorEngineeringCurrent (fluid)Electrical engineeringComputer scienceLimit (mathematics)VoltageControl theory (sociology)Electric power systemCircuit breakerControl (management)Power (physics)Physics

Abstract

fetched live from OpenAlex

Synchronous-machine DGs are well known to cause miscoordination of distribution system protections because of the generator's ability to contribute large fault currents to the fault. This paper proposes a field discharge circuit to limit the generator's fault current, thus leading to a synchronous-machine DG with little impact on distribution system protection. In this paper, the operation of a solid-state switch-based field discharge circuit is studied, and its effects on the generator's output current during the fault are investigated. It is shown that the proposed scheme removes the steady-state component of the fault current and accelerates the decay of the transient ac component of the current. The results demonstrate that the proposed field discharge circuit is sufficient to prevent miscoordination of the feeder protections when short time-delay and/or inverse-time overcurrent relays are involved in the protection scheme.

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)0.000
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.240
Teacher spread0.229 · 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

Citations68
Published2013
Admission routes1
Has abstractyes

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