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This research aims to enhance anomaly detection in SCADA systems for wind turbines using a hybrid machine learning framework.

July 14, 2026International Journal of Technology & Emerging ResearchOpen Access

Hybrid Machine Learning Framework for SCADA-Based Anomaly Detection in Wind Turbine Systems Using Ensemble Learning

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Randomized trial demonstrates improved anomaly detection in wind turbine systems, suggesting better predictive maintenance outcomes.

This research aims to enhance anomaly detection in SCADA systems for wind turbines using a hybrid machine learning framework.
Integrated Isolation Forest, One-Class Support Vector Machine, and Deep Autoencoder models
Used an AdaBoost-based stacking architecture to combine model outputs
Evaluated on a SCADA dataset with over 50,000 operational samples and multiple parameters.
Achieved Recall of 0.8702, F1-score of 0.7118, and AUC of 0.9678
Significantly reduced false negative predictions
Outperformed standalone machine learning approaches.

Ansari et al. (2026) studied this question.

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