Robust state control with narrowband disturbance rejection for wind driven DFIG under grid voltage unbalance
Bibliographic record
Abstract
This paper introduces a direct power control (DPC) method with narrowband disturbance rejection capability for the grid-connected doubly fed induction generator (DFIG). The method is suited for variable speed operation of DFIG, especially in wind energy conversion systems connected to weak networks where voltage unbalance is common. The control framework is based on the linear quadratic regulator theory with a frequency shaped cost functional method. The DFIG is represented in the synchronous reference frame as a multi-input, multi-output controlled plant. The control objective is to bring the stator active and reactive power errors to zero despite plant modeling errors and disturbances. A supplementary compensation loop is provided to alleviate the operational problems associated with grid voltage unbalance. The supplementary compensator is optimally designed by considering the whole closed-loop system dynamics. The proposed DPC operates at constant switching frequency and there is no need for rotor flux estimation and for subsynchronous or supersynchronous speed detection. Stability analyses together with simulation studies on 2 MW DFIG are provided to confirm the viability of the method.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".