Nonlinear Control Operation of DFIG-Based WECS Incorporated With Machine Loss Reduction Scheme
Bibliographic record
Abstract
This article presents a novel adaptive backstepping based nonlinear control scheme incorporated with machine loss reduction and parameter uncertainties for grid-connected doubly fed induction generator (DFIG) driven wind energy conversion system (WECS). The proposed nonlinear controller is developed to stabilize both the grid and rotor side current control loops of direct-drive DFIG-based WECS. Traditional feedback linearization controllers are sensitive to system parameter variations and disturbances on DFIG-based WECS, which demands advanced control techniques for stable and efficient performance considering the nonlinear system dynamics. The proposed nonlinear controller incorporates the system uncertainty and nonlinearities while ensuring the stability of the drive system through Lyapunov stability criteria. A machine loss reduction algorithm is also incorporated to achieve enhanced efficiency. The performance of the proposed nonlinear scheme is compared with conventional benchmark fixed gain proportional-integral control and sliding mode control scheme for the rotor-side converter controller. The proposed nonlinear controller for DFIG-based WECS integrated with machine loss reduction scheme is successfully implemented in real time using DSP board DS 1104 for a prototype 350 W DFIG. The simulation and experimental results prove the efficacy of the proposed scheme under variable operating conditions such as wind speed variation, grid voltage disturbances, and parameter uncertainties.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".