MétaCan
Menu
Back to cohort
Record W2105644046 · doi:10.1109/pes.2009.5275966

Novel method for estimating the CF of variable speed wind turbines

2009· article· en· W2105644046 on OpenAlexaff
Mohammed Albadi, Ehab F. El‐Saadany

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsExtrapolationWind speedVariable (mathematics)Wind powerTowerTurbineControl theory (sociology)Variable speed wind turbinePower (physics)Computer scienceMathematicsEngineeringMeteorologyStatisticsMathematical analysisAerospace engineeringArtificial intelligenceStructural engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a novel method for estimating the capacity factor (CF) of variable speed wind turbines using wind speed characteristics at any site and the power performance curve parameters of any variable speed wind turbine (VSWT). Compared to the existing model, the proposed CF formulation is simpler and results in more accurate CF estimation. The accuracy of the proposed CF model is verified using measured data. Moreover, the new formulation is modified to estimate the CF at any tower height. The accuracy of the modified CF model over that of the existing extrapolation one is verified using a case study.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.127
Threshold uncertainty score0.175

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 head0.283
Teacher spread0.260 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicWind Energy Research and DevelopmentFrench-language works237,207