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Record W2151330222 · doi:10.1109/pes.2006.1709576

Optimal tracking secondary voltage control for the DFIG wind turbines

2006· article· en· W2151330222 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

Venue2006 IEEE Power Engineering Society General Meeting · 2006
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsControl theory (sociology)Wind powerAC powerVoltage regulationInduction generatorVoltageVoltage optimisationVoltage droopCompensation (psychology)EngineeringMargin (machine learning)Electric power systemVoltage controllerVoltage regulatorComputer sciencePower (physics)Control (management)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents the development of the optimal tracking secondary voltage control based on determining of the regulation margin at all grid buses and using the intelligent selection based on the voltage violation condition of the proposed system. This OTSVC is more efficient to provide voltage regulation and achieve other benefits for voltage stability margin of the power system. The OTSVC can also employing with FACTS devices such as STATCOM for better voltage regulation and reactive power compensation. This paper considers an OTSVC for a doubly fed induction generator based wind park. The system is developed and simulated for six wind generators connected to a typical transmission system. The performance of the OTSVC is compared with the primary voltage control and secondary voltage control under the steady state and load excursion in a weak power 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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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