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Record W2120103362 · doi:10.1109/ccece.2007.360

A New Wind Power Plant Simulation Method to Study Power Quality

2007· article· en· W2120103362 on OpenAlexaff
Roohollah Fadaeinedjad, Gerry Moschopoulos, Mehrdad Moallem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsWestern University
FundersMinistry of Science Research and Technology
KeywordsWind powerTurbinePower optimizerMarine engineeringPower stationAerodynamicsWind profile power lawWind speedEnvironmental scienceAutomotive engineeringEngineeringMaximum power point trackingMeteorologyElectrical engineeringVoltageAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

In order to study the impact of a wind power plant on network power quality all electrical, mechanical and aerodynamic aspects of wind turbines must be studied. Moreover, the contribution of every wind turbine on the wind power plant should be considered. Representing a large wind power plant by a single wind turbine or a few wind turbines results in a severer situation with regards to power quality. In the paper, a new wind power plant representation is used to model the wind, mechanical and electrical parts of a wind power plant. Simulation results obtained from the model are used to observe the impact of wind fluctuations, wind shear, and tower shadow on the power and voltage at the point of common coupling.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.036
GPT teacher head0.367
Teacher spread0.331 · 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.

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

Citations11
Published2007
Admission routes1
Has abstractyes

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