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

作者：Zakir Ahmed Ansari, Dr. Tariq Siddiqui · 发表于：International Journal of Technology and Emerging Research · 年份：2026 · DOI：10.64823/ijter.2607006

Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in monitoring the operational health of modern wind turbines by continuously collecting large volumes of sensor data. Efficient analysis of this data is essential for early fault detection, predictive maintenance, and reliable turbine operation. However, anomaly detection in SCADA environments remains challenging due to data imbalance, noise, nonlinear relationships, and dynamic operating conditions. Traditional machine learning approaches often suffer from limited generalization capability and may fail to achieve a balanced trade-off between precision and recall. To address these challenges, this paper proposes a Hybrid Machine Learning Framework for SCADA-based anomaly detection in wind turbine systems using ensemble learning. The proposed framework integrates Isolation Forest, One-Class Support Vector Machine (OCSVM), and Deep Autoencoder models to capture complementary anomaly characteristics from operational data. The outputs of these base models are further combined using an AdaBoost-based stacking architecture to improve classification robustness and anomaly detection performance. Experiments were conducted on a publicly available wind turbine SCADA dataset containing more than 50,000 operational samples and multiple turbine health parameters. The proposed hybrid framework was evaluated using Precision, Recall, F1-score, Area Under Curve (AUC), and confusion matrix analysis. Experimental resul...