Dynamic-Tracking Damping Controller for DFIG-Based Wind Farms to Mitigate Sub-Synchronous Control Interactions
Bibliographic record
Abstract
This paper proposes a dynamic-tracking damping controller (DTDC) and designs an implementation scheme to alleviate the sub-synchronous control interactions (SSCI) in series-compensated networks integrated with doubly-fed induction generator (DFIG)-based wind farms. The DTDC can track sub-synchronous oscillatory current components under various operating conditions, providing high damping performance for DFIG wind turbines at a broader range of sub-synchronous frequencies. This is accomplished by designing an extra extended state observer (ESO)-based compensated loop for the rotor-side converter of DFIG, which enables the extraction and compensation of oscillatory current components at the sub-synchronous frequencies. In addition, the implementation scheme for the damping controller is designed based on the dissipated energy criterion, which can identify the weak damping wind farm and determine the number of wind turbines required to be installed. The proposed DTDC's effectiveness and implementation scheme are validated using the modified IEEE first benchmark model and a wind farm model in North China, respectively. The control hardware-in-loop (CHIL) experiments results demonstrate that the DTDC outperforms traditional approaches with superior damping and robustness under varying wind speeds, compensation levels, and the number of online wind turbines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".