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Impact of DFIG-based wind farms on generator distance phase backup protection

2017· article· en· W2570886448 on OpenAlexaff
Fei Sun, Yongzheng Zhang, S.O. Faried, Keaton A. Wheeler, Mohamed Elsamahy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRelayBackupInduction generatorGenerator (circuit theory)Fault (geology)Wind powerContext (archaeology)Line (geometry)Computer scienceAutomotive engineeringEngineeringControl theory (sociology)Electrical engineeringPower (physics)MathematicsPhysics

Abstract

fetched live from OpenAlex

The paper explores the impact of large doubly-fed induction generator (DFIG)-based wind farms on the performance of generator distance phase backup protection (Relay (21)) and the coordination between this relay and the generator capability limits. In this context, investigations are conducted on a sample power system incorporating a large DFIG-based wind farm connected to a transmission line. The results of these investigations show that a wind farm connected to a transmission line has an adverse effect, not only on the distance phase backup protection of a nearby generator but also on the coordination between such a protection and the generator steady-state capability limits. The severity of such an impact depends on the fault type and its location. The time-domain simulation studies are carried out using the EMTP/RV.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.021
GPT teacher head0.299
Teacher spread0.277 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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