Research on Wind Farms Aggregation Method for Electromagnetic Simulation Based on FDNE
Bibliographic record
Abstract
In this work, a wind farm aggregation method for electromagnetic simulation model based on FDNE is proposed. Identical subsystems terminated with a same node operating in parallel can be aggregated with the same constraints. WTGs connected to the same feeder within a wind farm can be aggregated as one equivalent subsystem. And the wind farm aggregated model includes several aggregated subsystems based on the grouping criterion. And each aggregated subsystem model constitutes an aggregated WTG with a current amplifier to generate the same amount of real power, an equivalent impedance of collector system, and a FDNE component to adjust the aggregated model frequency characteristics. Wind farms based on DFIG and PMSG are used to validate the effectiveness of the proposed aggregation method. The simulation comparison results indicate that the proposed method can guarantee the consistencies of the power flow and transient responses during faults with high accuracy. And the computation time is shortened by 87% in the dome cases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".