Weighted Dynamic Aggregation Modeling of Induction Machine-Based Wind Farms
Bibliographic record
Abstract
This paper presents Weighted Dynamic aggregation (WD agg) method to obtain an equivalent Wind Turbine Generator (WTG) for an induction machine-based wind farm using its dynamic model. The suggested approach obtains the equivalent d-q model of the induction generators considering the contribution of each unit in the model. The challenges in the aggregation of a large-scale wind farm are the variation of wind speeds at different zones and differences in the WTGs parameters. Compared with the existing methods such as Full aggregation (Full agg), Zone aggregation (Zone agg), and Semi aggregation (Semi agg), the suggested WD agg method provides an accurate single unit equivalent model for a large-scale wind farm while taking into account various wind speed zones and unequal WTG parameters. The proposed method is evaluated through time-domain simulation of a 4-WTG and a large-scale 20-WTG Doubly-Fed Induction Generator (DFIG) wind farms and their aggregated models. These simulations cover combinations of different wind speeds and WTGs parameters. Also, a 4-WTGs fixed-speed wind farm is studied to show the generality of the proposed method. Comparing WD model with the detailed response of the wind farm verifies the accuracy of the method in both steady-state and transient behaviors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".