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Record W2914719619 · doi:10.1109/tpwrd.2019.2896135

Impact of Wind Generation on Power Swing Protection

2019· article· en· W2914719619 on OpenAlexaff
Aboutaleb Haddadi, Ilhan Koçar, Ulas Karaagac, Henry Gras, Evangelos Farantatos

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2019
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsPolytechnique Montréal
FundersElectric Power Research Institute
KeywordsTrippingSwingWind powerElectric power systemElectricity generationFault (geology)EngineeringPower-system protectionPower (physics)Automotive engineeringElectrical engineeringReliability engineeringComputer scienceCircuit breakerMechanical engineering

Abstract

fetched live from OpenAlex

Large-scale integration of wind generation changes the power swing characteristics of a power system and may result in the misoperation of legacy power swing protection schemes. This paper presents a qualitative study on the impact of wind generation on power swing protection. The objective is to provide an understanding of the problem through case studies and present possible solutions and adjustments in protection schemes to ensure the efficiency of protection under large-scale integration of wind generation. The misoperation of power swing protection functions, namely power swing blocking and out-of-step tripping (OST), as a result of increased wind generation levels, are shown through case studies. It is also shown that the electrical center of a power system may move due to wind generation. In this case, it would be necessary to revise the optimal location of the OST protection. Finally, the impact of various factors, such as wind generator type, control scheme and fault-ride-through function, and wind generation level and capacity are investigated to determine the key features that need to be accounted for in practical protection studies.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.222
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

Citations72
Published2019
Admission routes1
Has abstractyes

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