MétaCan
Menu
Back to cohort

A Modified PUTT Scheme for Transmission Lines Connecting DFIG-Based Wind Farm

2021· article· en· W4205411452 on OpenAlexaff
Zhang Xiaoyou, Sahar Pirooz Azad

Bibliographic record

Venue2021 IEEE Power & Energy Society General Meeting (PESGM) · 2021
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCrowbarRelayInduction generatorWind powerElectric power transmissionTransmission (telecommunications)Fault (geology)Control theory (sociology)Doubly fed electric machineGridTurbineComputer scienceEngineeringElectrical engineeringPower (physics)AC powerVoltageMathematicsPhysics

Abstract

fetched live from OpenAlex

With the large integration of wind turbine generators (WTGs) in the power grid, the protection of the transmission lines connecting to the WTGs has become a great concern. Doubly fed induction generators (DFIGs) are among the most widely installed WTGs, but the frequency deviation of the fault current measured at the wind farm side relays due to the short circuit behavior of DFIGs with the activation of the crowbar circuit has resulted in the failure of distance relays used for protection of transmission lines connecting DFIGs. This paper proposes a modified permissive underreaching transfer trip (PUTT) scheme that provides fast and reliable protection for the transmission lines connecting DFIG-based wind farms. By adding the frequency tracking elements to the conventional PUTT scheme, the modified scheme correctly blocks the maloperation of the distance elements during external faults, enables the trip of the relay during internal faults, and significantly improves the security of the relay at the wind farm side.

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)0.001
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.246
Teacher spread0.225 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venue2021 IEEE Power & Energy Society General Meeting (PESGM)Same topicIslanding Detection in Power SystemsFrench-language works237,207