Enhancing Wind Energy Conversion System Performance via Sliding Mode Control and Parameter Estimation with PI-MRAS
Bibliographic record
Abstract
The purpose of this paper is to investigate a wind power system utilizing a doubly fed induction generator (DFIG).In this configuration, the stator is directly connected to the grid, while the rotor is linked to the grid via a back-to-back converter.The primary aim is to develop a decoupled control system for the DFIG to improve power quality.To achieve this goal, we introduce a robust control technique as a means to control the reactive and active power of DFIG.This technique is known as sliding mode control.Furthermore, we propose a model-reference adaptive system estimator based on proportional and integral controllers (PI-MRAS) for sensor-less control of DFIG.This estimator is designed to accurately approximate the rotor resistance.The proposed control strategies enhance the performance of the wind energy conversion system, particularly considering variations in the machine's parameters.Simulation results demonstrate the high performance and robustness of control strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".