Lyapunov Function Based Flux and Speed Observer Using Advanced Non-linear Backstepping DVC for PWM-Rectifier Connected Wind-turbine-driven PM Generator
Bibliographic record
Abstract
In this paper, modeling, and speed/position sensor-less designed Direct Voltage Control (DVC) approach based on the Lyapunov function are studied for three-phase voltage source Space Vector Pulse Width Modulation (SVPWM) Rectifier Connected to a Permanent Magnet Synchronous Generator (PMSG) Variable Speed Wind Power Generation System (VS-WPGS). This control strategy is based on voltage orientation technique without mechanical speed sensor. Advanced Non-linear Integral Backstepping Control (IBSC) of the Generator Side Converter (GSC) has the ability to have a good regulation of the DC link voltage to meet the requirements necessary to achieve optimal system operation, regardless of the disturbances caused by the characteristics of the drive train or some changes into the DC load. The estimation of the speed is based on Model Reference Adaptive System (MRAS) method. This method consists in developing two models one of reference and the other adjustable for the estimation of the two d-q axis components of the stator flux from the measurement of currents, the speed estimated is obtained by canceling the difference between the reference stator flux and the adjustable one using Lyapunov criterion of hyper-stability. Some results of simulation using Matlab/Simulink® are presented, discussed to prove the efficiency and robustness of the system control policy for WPGS against external and internal perturbations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".