MétaCan
Menu
Back to cohort
Record W2604968364 · doi:10.7836/kses.2016.36.6.001

Design of Power and Load Reduction Controller for a Medium-Capacity Wind Turbine

2016· article· en· W2604968364 on OpenAlexaff
Kwansu Kim, Ju-Hee Kim, Insu Paek, Cheol‐Jin Kim, Hyun‐Gyu Kim, Hyoung-Gil Kim

Bibliographic record

VenueJournal of the Korean Solar Energy Society · 2016
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsNacelleTurbineControl theory (sociology)Tip-speed ratioThrustController (irrigation)Blade pitchAerodynamicsVariable speed wind turbineWind speedWind powerPower (physics)Permanent magnet synchronous generatorEngineeringComputer scienceMechanical engineeringMagnetAerospace engineeringPhysicsElectrical engineeringMeteorologyControl (management)

Abstract

fetched live from OpenAlex

A control algorithm for a 100 kW wind turbine is designed in this study. The wind turbine is operating as a variable speed variable pitch (VSVP) status. Also, this wind turbine is a permanent magnet synchronous generator (PMSG) Type. For the medium capacity wind turbine considered in this study, it was found that the optimum tip speed ratios to achieve the maximum power coefficients varied with wind speeds. Therefore a commercial blade element momentum theory and multi-body dynamics based program was implemented to consider the variation of aerodynamic coefficients with respect to Reynolds numbers and to find out the power and thrust coefficients with respect tip speed ratio and blade pitch angles. In the end a basic power controller was designed for below rated, transition and above rated regions, and a load reduction algorithm was designed to reduce tower vibration by the nacelle motion. As a result, damage equivalent Load (DEL) of tower fore-aft has been reduced by 32%. From dynamic simulations in the commercial program, the controller was found to work properly as designed. Experimental validation of the control algorithm will be done in the future.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.557
Threshold uncertainty score0.230

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)0.0000.000

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 head0.014
GPT teacher head0.205
Teacher spread0.191 · 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 designBench or experimental
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

Citations17
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Korean Solar Energy SocietySame topicWind Energy Research and DevelopmentFrench-language works237,207