Aggregation Equivalence and Evaluation Method of Multiple Doubly-fed Wind Farms for Subsynchronous Oscillation Characteristics Analysis
Bibliographic record
Abstract
The problem of subsynchronous oscillation (SSO) caused by large-scale wind farm integration seriously poses a threat to the safe and stable operation of high-proportion renewable energy power systems. To reduce the order of the system model and improve the simulation efficiency, this paper puts forward an aggregation equivalence and evaluation method of multiple doubly-fed wind farms for SSO characteristics analysis. Firstly, an aggregation equivalence method for multiple doubly-fed wind farms is proposed. The main influencing factors in the SSO analysis are taken as the clustering objects, and the simulated annealing (SA) algorithm and fuzzy c-means (FCM) clustering algorithm are combined to quickly obtain the wind farm clustering sets and equivalent model parameters. Secondly, the evaluation index of the wind farm equivalent models is proposed. The optimal equivalent scheme is selected by comparing the impedance characteristic curves of the equivalent model with that of the detailed model. Finally, taking the actual project as an example, the effectiveness of the proposed method for SSO characteristics analysis is verified.
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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 | 0.001 | 0.001 |
| 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".