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Record W2123958848 · doi:10.1109/pes.2006.1709255

Nonlinear state space modeling of a variable speed wind power generation system

2006· article· en· W2123958848 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 institutionsUniversity of Calgary
Fundersnot available
KeywordsNonlinear systemControl theory (sociology)State spaceState-space representationVariable (mathematics)State variableComputer scienceWind powerRepresentation (politics)Wind speedTrajectoryLinear modelMathematicsEngineeringControl (management)AlgorithmPhysicsArtificial intelligenceMeteorology

Abstract

fetched live from OpenAlex

New problems emerging from the increased production of power from renewable resources are becoming a good challenge. To face the new challenges, accurate modeling of such systems is required. For example, to be able to apply advanced control strategies, usually a linearized state space representation of the system is needed. Taking a variable speed cage machine wind generation system as the case study, in this paper a nonlinear state space model is developed first and then linearized. As the wind speed changes randomly, the parameters of the equivalent linear model also change to track these changes. Results of simulation studies show that the linearized model matches well with the original nonlinear state space model. The nonlinear and linear state space models derived in this paper can be used in the development of advanced control strategies

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 score0.323
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.176
Teacher spread0.170 · 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