Optimal power tracking control of a hydraulic wind turbine based on active disturbance rejection control methodology
Bibliographic record
Abstract
Power point tracking (PPT) is one of the necessary functions of the wind turbine to optimize the use of wind energy. PPT is a condition that needs to be completed after the grid is connected, which can be achieved by tracking the optimal rotation speed of the output of the wind turbine and the optimum torque and power output of the hydraulic system. Based on a fixed displacement pump speed control, an optimal PPT strategy with the active disturbance rejection control (ADRC) method is proposed, and the control objective is to maximize the energy conversion of the system. This paper sets out to (i) establish a hydraulic wind turbine grid-connected affine nonlinear mathematical model, based on the ADRC method and a fixed displacement pump speed output control, (ii) design a nonlinear tracking differentiator and extended state observer and nonlinear state error feedback control law, and (iii) achieve optimal PPT under different wind speeds. Simulations were model by MATLAB/Simulink, where the system inputs signals of different wind speeds and analyses control system stability and robustness. Simulation results show that the input power was greater with a fixed displacement pump speed .
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".