Nonlinear Integral Backstepping Control of DFIGs-based Wind Farm under Unbalanced Electrical Grid Voltage
Bibliographic record
Abstract
This paper proposes a modified and efficient control strategy for controlling a doubly fed induction generator (DFIG) based wind farm (WF) connected to an unbalanced electrical grid voltage. This strategy has two loops, the first is main and the second is auxiliary. The main loops control the positive sequence currents of the rotor and the grid side converter (GSC), while the auxiliary loops control the negative sequence currents. In this work, the positive and negative rotor current loops of each DFIG were controlled using a nonlinear integral backstepping control (IBSC) algorithm, while the positive and negative sequence current loops of each GSC were controlled using a proportional-integral (PI) controller. This study was validated by comparing the proposed strategy with the single-loop strategy through simulation in the Matlab/Simulink environment. The obtained results show that the proposed strategy allows to reduction of the oscillations in the active and reactive power generated by the WF, which reduces the oscillations in the DC bus voltage and reduces the total harmonic distortion.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".