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Record W2083497871 · doi:10.1109/powercon.2010.5666065

Opportunities and challenges of high penetration wind power

2010· article· en· W2083497871 on OpenAlexaff
Boon‐Teck Ooi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsWind powerTurbineFlywheelAutomotive engineeringPenetration (warfare)Marine engineeringGrid energy storageRenewable energyPower optimizerEnvironmental scienceWind hybrid power systemsPumped-storage hydroelectricityElectrical engineeringComputer scienceEngineeringDistributed generationAerospace engineeringVoltageMaximum power point tracking

Abstract

fetched live from OpenAlex

Wind farms possess two assets which have potential for further economic exploitation: (1) the power electronic frequency changers in the front ends of the wind-turbine generators (WTGs); (2) the very large moments of inertia of the wind turbine blades. When wind penetration will be 20%, the electric power grid will have 20% power electronic control which can be used in the same way as FACTS for dynamic performance enhancement. The wind turbine blades are inbuilt flywheels whose kinetic energy storage can be used as primary reserve and as buffer to reduce cyclical stress which can cause fatigue failure of wind-turbine generators.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score0.197

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 head0.023
GPT teacher head0.191
Teacher spread0.168 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2010
Admission routes1
Has abstractyes

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