Integral sliding mode control for back‐to‐back converter of DFIG wind turbine system
Bibliographic record
Abstract
In this study, an integral sliding mode control approach for controlling the power electronics converters of a doubly‐fed induction generator (DFIG) wind turbine system is presented. The power electronics interface consists of back‐to‐back converters. The rotor side converter regulates the active and reactive powers at the DFIG stator through controlling the stator currents. The stator current dynamics, with respect to the rotor voltages, is developed from the conventional equations of the DFIG model. In this control configuration, the knowledge about the rotor currents is not required, which reduces the use of the current measurement sensors. The grid side converter ensures constant dc‐link voltage while transferring the power from the DFIG rotor to the grid. The proposed control approach uses a composition of sliding mode and integral parts to improve the overall performance and robustness against parametric variations and uncertainties. A lab‐scale DFIG wind turbine system is used to investigate the proposed control approach efficiency under various operating conditions. The experimental results show the effectiveness of the proposed control approach in achieving control objectives to operate the DFIG wind turbine system.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".