Robust DC-Link Voltage Control of a Full-Scale PMSG Wind Turbine for Effective Integration in DC Grids
Bibliographic record
Abstract
DC grids are gaining widespread acceptance under the smart grid paradigm. A full-scale permanent-magnet synchronous generator wind turbine can be interfaced to a dc grid by a voltage-source converter (VSC). Under weak ac grid connections or isolated operation of the dc grid, the machine-side VSC regulates the dc-link voltage via changes in the generator speed. However, several control difficulties are yielded; important among these are: 1) the nonlinear plant dynamics with wide range of operating point variation; 2) the control lever is mainly the generator speed, which complicates the dc-link voltage control dynamics; 3) the presence of uncertain disturbances associated with dynamic loads (e.g., power-converter-based loads) connected to the dc grid and wind speed variation; and 4) the presence of parametric uncertainty associated with the equivalent dc-link capacitance due to connecting/disconnecting converter-based loads. This paper presents a robust dc-link voltage controller that addresses such control difficulties. The proposed controller is based on the robust structured singular values (μ)-synthesis approach and a functional fuzzy model. Analysis and comparative simulation and experimental results validate the effectiveness of the proposed controller.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".