Current source converter-based offshore wind farm: configuration, modulation, and control
Bibliographic record
Abstract
Offshore wind power is attracting increased attention because of considerable wind resources, higher and steadier wind speeds, and smaller environmental impact. Recently, a current source converter (CSC)-based series-connected configuration is proposed and it is considered a promising solution for offshore wind farms as the offshore substation used in existing systems can be eliminated. However, such a CSC-based configuration has disadvantages in terms of size and weight, dynamic performance, cost, reliability, and efficiency. Therefore, this thesis proposes new configurations, modulation scheme, and control schemes to improve the performance of the CSC-based offshore wind farm. First, a new configuration is proposed for the CSC-based offshore wind farm. Compared with existing CSC-based configurations, the new one is expected to be smaller in size and weight. Second, conventional space vector modulation (SVM) with fast dynamic response cannot be used for grid-side CSCs because of its high-magnitude low-order harmonics. To solve this issue, an advanced SVM with superior low-order harmonics performance is proposed. Third, power balancing among series-connected CSCs is an important consideration for system reliability. The possible imbalance of power is investigated and quantitatively defined. A power balancing scheme is proposed, based on which equal power distribution among CSCs is ensured. Fourth, to lower the system insulation requirement of the CSC-based configuration, a bipolar operation is investigated. Compared with monopolar mode, bipolar mode gives lower insulation level, thus contributing to the system in terms of lower cost and higher reliability for a given power rating. In addition, an optimal dc-link current control giving higher efficiency is proposed for the bipolar system. Fifth, an optimized control strategy with reduced cost and improved efficiency is proposed for the CSC-based offshore wind farm. The nominal number of onshore CSCs is optimized, which reduces the cost on power converters. And an optimized bypass operation is introduced to onshore CSCs, which improves the efficiency of the system. Finally, simulation and experimental results are provided to verify the performance of the proposed configuration, modulation scheme, and control schemes.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".