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Maximum power point tracking controller using Lyapunov theorem of wind turbine under varying wind conditions

2022· article· en· W4311537634 on OpenAlexaff
Maamar Yahiaoui, Benameur Afif, Brahim Brahmi, Mohamed Horch, Mohamed Serraoui

Bibliographic record

VenueInternational Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsCollege Ahuntsic
Fundersnot available
KeywordsControl theory (sociology)Controller (irrigation)Lyapunov functionWind speedLyapunov stabilityTip-speed ratioTurbineWind powerVariable speed wind turbineLyapunov redesignRotational speedMaximum power principleComputer sciencePower (physics)PID controllerMathematicsControl engineeringPhysicsEngineeringPermanent magnet synchronous generatorControl (management)Nonlinear systemTemperature control

Abstract

fetched live from OpenAlex

Due to the instantaneous variation in wind speed, it is necessary to identify the optimal rotational speed that ensures maximum energy efficiency and system stability. We proposed a controller based on the Lyapunov theorem to extract the maximum power from wind speed and to ensure the overall stability of the controlled system under random operating conditions imposed by wind speed and parameter variations. The control of the Tip speed ratio is based on the Lyapunov theorem (TSR_LT), which is a controller based on Lyapunov's theory and the definition of a positive, energetic function, to ensure the stability of the system being controlled, the dynamics of this function must be negative. The viability of this work is demonstrated by MATLAB-based mathematical and simulation models and a comparison with the results obtained using proportional integral (PI) controller-based tip speed ratio control (TSR_PI controller). The simulation results demonstrate the controller's effectiveness.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.224
Teacher spread0.217 · 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
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

Citations2
Published2022
Admission routes1
Has abstractyes

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