An Iterative Cleaning Method for Abnormal Wind Power Data in Wind Farms Based on Wasserstein Distance
Bibliographic record
Abstract
A wind turbine's power curve is an important indicator for evaluating the power generation performance of wind turbines, which is of great significance for the operation of wind farms and the scheduling of power systems. However, the shutdown of wind turbines, sensor failures, and power curtailment can cause a large number of outliers, which poses great challenges to wind turbine status monitoring and power prediction. Aiming at the characteristics of abnormal wind turbine data, this paper proposes an iterative cleaning method for wind farms based on wasserstein distance. Via this proposed method, the speed and power are modeled while gradually removing outliers. A neural network combined with wasserstein distance and monotonic constraints is leveraged to create a curve model and to synchronously clean up abnormal data. The curve fitted by the neural network converges to the true wind turbine power curve, which ultimately enables curve modeling while removing outliers. Finally, various experiments are conducted on numerical simulation datasets and twelve real wind turbine datasets. Qualitative and quantitative results demonstrate that the algorithm proposed can establish an accurate power curve model in the presence of a large amount of abnormal data, and significantly outperforms other baselines based on some discussed criteria.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".