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Record W652055859 · doi:10.1109/epec.2015.7379919

Fault ride-through capability of doubly-fed induction generators based wind turbines

2015· article· en· W652055859 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCrowbarInduction generatorControl theory (sociology)Fault (geology)Wind powerMATLABController (irrigation)Computer scienceGridVoltageDoubly fed electric machineLow voltage ride throughAC powerControl engineeringEngineeringControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

Current grid codes require DFIGs to provide voltage support during a grid fault. The fault ride-through (FRT) capability of DFIGs is the focus of this paper, in which modifications to the DFIG controller are proposed to improve the FRT capability. The static synchronous compensator (STATCOM) controller has been applied with the proposed method to study its influence on the voltage at the point of common coupling (PCC). The proposed method was also compared with other FRT capability improvement methods, including the conventional crowbar method. The dynamic behavior of DFIG-based wind turbines during grid fault is simulated using MATLAB/Simulink. The results obtained clearly demonstrate the efficacy of the proposed method.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.482
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.229
Teacher spread0.203 · 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

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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