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Record W2035573351 · doi:10.1109/ccece.2008.4564810

Output power maximizing of a wind turbine by adjusting rotor speed

2008· article· en· W2035573351 on OpenAlex
Tin Luu, Adel Nasiri

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsTurbineWind speedVariable speed wind turbineWind powerControl theory (sociology)Rotor (electric)Induction generatorMATLABPower (physics)Electronic speed controlPower optimizerMaximum power principleGenerator (circuit theory)Computer scienceEngineeringAutomotive engineeringMaximum power point trackingPermanent magnet synchronous generatorVoltageElectrical engineeringControl (management)PhysicsAerospace engineering

Abstract

fetched live from OpenAlex

This paper presents a control technique to maximize the output power of a wind turbine by adjusting the rotor speed. The control method is applied to a Doubly-Fed Induction Generator (DFIG). The modeling of DFIG is performed using Matlab/Simulink. The wind speed is modeled based on Kaimal spectra. Since DFIG is a variable speed generator, the rotor speed can be regulated using the power electronics interface to extract the maximum power from wind. The wind turbine system is modeled at three different speeds to verify the viability of this control technique.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.013
GPT teacher head0.169
Teacher spread0.156 · 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