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Record W2103437836 · doi:10.1177/0959651812462328

Nonlinear model predictive controller with state observer for speed sensorless induction generator–wind turbine systems

2012· article· en· W2103437836 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

VenueProceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsRoyal Military College of CanadaSaint Mary's University
Fundersnot available
KeywordsControl theory (sociology)Observer (physics)Nonlinear systemWind powerTurbineController (irrigation)Induction generatorVariable speed wind turbineState observerRotor (electric)TorqueWind speedModel predictive controlControl engineeringElectronic speed controlLyapunov functionLyapunov stabilityEngineeringComputer sciencePermanent magnet synchronous generatorControl (management)VoltagePhysics

Abstract

fetched live from OpenAlex

In this article, the problem of tracking control for variable speed induction generator–wind energy conversion system is investigated using nonlinear predictive control. A rotor speed predictive control algorithm has been designed to control the angular speed of the machine in order to allow the wind energy conversion system to operate with maximum power extraction. The generator torque and uncertainties are estimated and injected into the control law to improve the tracking performance. Control action is carried out assuming that all the states are known by measurement. Then, a state observer is implemented and Lyapunov method is used to prove the global stability of the complete continuous control scheme. Simulation is carried out to verify the performance of the proposed control system.

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 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: none
Teacher disagreement score0.726
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.191
Teacher spread0.180 · 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