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CROWBAR RESISTANCE SETTING AND ITS INFLUENCE ON DFIG LOW VOLTAGE RIDE THROUGH

2017· article· en· W2783605161 on OpenAlexvenueno aff
Shengqing Li, Yao Ming, Yuwen Zhang, Wenfeng Wu

Bibliographic record

VenueInternational Journal of Robotics and Automation · 2017
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsnot available
Fundersnot available
KeywordsCrowbarDoubly fed electric machineVoltageResistance (ecology)Electrical engineeringComputer scienceAutomotive engineeringAC powerEngineering

Abstract

fetched live from OpenAlex

Doubly-fed induction generator (DFIG) often adopts a crowbar protective circuit to realize low-voltage ride through (LVRT) under low-voltage fault of the grid.Crowbar resistance will affect the LVRT effect of the unit significantly.Time domain expression of transient current of the wind turbine generator system under the voltage drop fault and the calculation formula of fault current at the rotor side were deduced through space vector analysis and Laplace transformation based on the transient mathematical model of the DFIG system under voltage drop.A crowbar resistance adjustment method conforming to engineering practices was proposed, which overcame the overcurrent at the rotor side and overvoltage of the DC bus after the crowbar protective circuit is applied.The case study and the simulation test demonstrated that the proposed method inhibits the transient fault current component effectively and can increase the automation level of the DFIG system.

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)0.003
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.254
Teacher spread0.247 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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