Wirtschaftliche Leistungsregulierung für Offshore-Windparks mit Ringnetz
Bibliographic record
Abstract
The Canadian developer and owner of green power facilities Northland Power Inc. owns two offshore wind farms (OWFs) in the German Bight, Deutsche Bucht and Nordsee One, operated by the subsidiary Northland Power Europe GmbH. The company supports and conducts research in the field of power flow optimization in wind farm networks. The work at hand represents the results of this effort to maximize the efficiency of the assets. The project was accomplished within a cooperation between the Nordsee One GmbH and the Institute of Electrical Power and Energy Technology at the Hamburg University of Technology. From an external point of view, an OWF represents an "en bloc"power plant connected to the onshore transmission grid via power export cables. Nevertheless, such a power plant comprises a complex, large-scale internal medium voltage network. In case of failure or cable outage, the network topology of an OWF may be modified and unintended overloading of inter-array cables (IACs) is possible. In order to address this issue, a new algorithm and software tool for economic power control in OWFs are introduced in the following which can be employed in wind farms with integrated loop connection cables (LCCs). This configuration particularly entails the risk of overloading cable segments depending on the present wind speed. The new algorithm provides the operator with adapted active power setpoints for each wind turbine generator (WTG) in a given network topology. The aim is to maximize OWF power generation and minimize internal power losses while secure network operation is guaranteed. Using load flow analysis based on WTG power output measurements, the load on each cable section is monitored and the cables can be utilized to their individual full capacity while overload is avoided. The practicability of the approach is demonstrated by means of simulation results.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.023 |
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".