Impacts of a LVRT Control Strategy of Offshore Wind Farms on the HTS Power Cable
Bibliographic record
Abstract
High temperature superconducting (HTS) power cables are a potential solution for the grid integration of offshore wind farms since the HTS cable can conduct bulk wind power at low voltage levels. However, the transient current through the HTS cable in cases of low voltage ride through (LVRT) operation has a negative impact on the HTS cable operation due to the quenching phenomenon. This paper analyzes the impact of LVRT control strategies on the HTS cable operation. In addition, a coordinated control of wind turbines for LVRT improvement of an offshore wind farm is proposed. The feasibility of the HTS cable application for the grid connection of offshore wind farms is also discussed in this study. The proposed controller is designed for the wind turbine generator based on a type-4 permanent magnet synchronous generator. In the proposed controller, the transient current through the HTS cable is reduced by regulating the machine side power during fault conditions. The feasibility of the proposed controller is validated in the PSCAD/EMTDC program (Manitoba Hydro International Ltd., Winnipeg, Manitoba, Canada, version 4.2.1). The effects of transient current on the cable temperatures and resistances are analyzed in this study. Simulation results show that the proposed control strategy could reduce the transient current and temperature rise of the HTS cable.
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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) | 0.001 | 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".