Wind Turbine SCADA Data Imbalance: A Review of Its Impact on Health Condition Analyses and Mitigation Strategies
Bibliographic record
Abstract
The rapidly increasing installed capacity of Wind Turbines (WTs) worldwide emphasizes the need for Operation and Maintenance (O&M) strategies favoring high availability, reliability, and cost-effective operation. Optimal decision-making and planning are supported by WT health condition analyses based on data from the Supervisory Control and Data Acquisition (SCADA) system. However, SCADA data are highly imbalanced, with a predominance of healthy condition samples. Although this imbalance can negatively impact analyses such as detection, Condition Monitoring (CM), diagnosis, and prognosis, it is often overlooked in the literature. This review specifically addresses the problem of SCADA data imbalance, focusing on strategies to mitigate this condition. Five categories of such strategies were identified: Normal Behavior Models (NBMs), data-level strategies, algorithm-level strategies, cost-sensitive learning, and data augmentation techniques. This review evidenced that the choice among these strategies is mainly dictated by the availability of data and the intended analysis. Moreover, algorithm-level strategies are predominant in analyzing SCADA data because these strategies do not require the costly and time-consuming task of data labeling. An extensive public SCADA database could ease the problem of abnormal data scarcity and help handle the problem of data imbalance. However, long-dated requests to create such a database are still unaddressed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".