Coordinated Control of Cascaded Current-Source Converter Based Offshore Wind Farm
Bibliographic record
Abstract
Offshore wind farms with cascaded PWM current-source converters (CSCs) at both generator- and grid-side can eliminate the need for bulky central offshore converter platform, which is usually used in a voltage-source converter (VSC) based counterpart. This novel system structure can simplify the system configuration and operation. However, the wind speed inconsistency at each turbine causes different dc-link current requirements for each CSC. This causes a considerable challenge for systems in which each CSC shares equal dc-link current. In order to overcome the problem, a coordinated control scheme for the dc-link current regulation, which considers wind speed difference of each turbine, is proposed. This control scheme enables the system to operate at minimum dc-link current, contributing to a lower operation losses. In the meantime, the independent control capability of each generator is guaranteed (e.g., maximum power tracking to make full utilization of available wind energy). Furthermore, the whole wind farm control strategy, which consists of wind farm supervisory control (WFSC), local wind turbine control and centralized grid control, is investigated and studied, where maximum power tracking and power limitation modes can be easily achieved. Both simulation and experimental verification of the proposed system with use of two permanent-magnet synchronous generators (PMSGs) are provided.
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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) | 0.001 | 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 | 0.000 |
| 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".