Jul 2026· International Journal of Technology and Emerging Research· Vol 2, pp. 53-65· 0 citations· 14 references
TL;DR
This paper proposes a Hybrid Machine Learning Framework for SCADA-based anomaly detection in wind turbine systems using ensemble learning, which significantly outperforms standalone approaches and substantially reduces false negative predictions.
Abstract
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 results demonstrate that the proposed model significantly outperforms standalone approaches, achieving a Recall of 0.8702, F1-score of 0.7118, and AUC of 0.9678. Furthermore, the framework substantially reduces false negative predictions, making it highly suitable for predictive maintenance applications. The findings indicate that integrating machine learning, deep learning, and ensemble learning techniques provides a robust and effective solution for intelligent anomaly detection in industrial SCADA systems.
Keywords: machine learning; deep learning; Anomaly detection; AutoEncoder; Ensemble Learning; One-Class SVM; SCADA; Wind Turbine Monitoring; Isolation Forest; Predictive Maintenance
A new stacking-ensemble hybrid machine learning model that will combine a one-dimensional convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) module, a Random Forest classifier, and an XGBoost gradient booster as base learners under the guidance of a logistic regression meta-learner is suggested.
A. Gopalakrushna· Materials Research Proceedin...· 0 citations
The rapid expansion of renewable energy systems demands reliable fault detection and prediction to ensure operational efficiency and grid stability. This study presents a novel framework that integrates Extended Kalman Filter (EKF) state estimation with uncertainty-aware graph learning for photovoltaic (PV) array fault detection and localization. Raw sensor data are processed by the EKF to generate refined state estimates and uncertainty covariances for each PV module. These uncertainty measures dynamically modulate an attention-based graph construction module, enabling adaptive edge weighting that down-weights unreliable connections during noisy or transient conditions. The resulting dynamic graphs are analyzed by a temporal graph attention network to produce both node-level fault localization and global anomaly scores. The graph-construction, temporal-encoding, and prediction components were optimized jointly, while the EKF process and observation models and their noise covariances remained fixed after calibration. On the real-world dataset, it attains an AUC-ROC of 0.941 and F1-score of 0.918 for global detection, and a node-level F1-score of 0.865 with Exact Match Ratio of 0.738 for fault localization. The approach demonstrates strong robustness to sensor noise and transient faults by leveraging physical uncertainty to guide graph topology. This work offers a promising direction for reliable monitoring of large-scale PV systems and other sensor-rich energy infrastructures.
Saud Wasly, N. Abu-Hamdeh· Scientific Reports· 0 citations
A novel self-supervised strategy for effective single-machine training based on classifying the distance between the monitored machine and each microphone sensor of a multi-channel recording system is introduced, providing a cost-efficient and privacy-preserving alternative while delivering competitive detection performance.
Erich Malan, Valentino Peluso, A. Calimera et al.· IEEE Access· 0 citations
The rapid expansion of renewable energy infrastructures has introduced significant challenges for monitoring system performance, ensuring regulatory compliance, and maintaining transparency in energy production and emissions reporting. Modern renewable energy systems generate large volumes of heterogeneous operational data through smart meters, sensor networks, supervisory control and data acquisition (SCADA) systems, and distributed generation platforms. Traditional monitoring approaches, which rely primarily on rule-based thresholds and periodic audits, often struggle to process such complex and dynamic data streams in real time. This paper proposes an artificial intelligence (AI)-driven intelligent monitoring framework designed to enhance operational oversight and sustainability monitoring in renewable energy systems. The proposed architecture integrates machine learning and anomaly detection techniques to analyze energy production data, detect abnormal operational patterns, and assess compliance with environmental and regulatory requirements. A design-science research methodology is adopted to develop and evaluate the framework using simulated renewable energy datasets representing solar and wind energy production scenarios. Experimental results demonstrate that the AI-based monitoring system significantly improves anomaly detection accuracy and reduces reporting delays compared with traditional rule-based monitoring methods. The proposed approach supports intelligent renewable energy infrastructure management by enabling proactive monitoring, improved operational transparency, and enhanced sustainability reporting.
Badreddine Said, Ashraf Rashid, Omari Asem et al.· E3S Web of Conferences· 1 citation
A transferred SISA (Sharded, Isolated, Sliced, and Aggregated) fault diagnosis framework is developed and applied to rolling bearing data, demonstrating a 84.32% decrease in retraining time compared to non-SISA full-retraining while restoring accuracy to the pre-poisoning SISA level.
Emily Yin, Jingyi Yan, Nanhong Liu et al.· 0 citations
Hydrogen refueling station (HRS) requires continuous safety monitoring, yet conventional management relies largely on periodic inspection and manual oversight, limiting proactive risk mitigation. This study presents a data-driven intelligent analysis platform for real-time monitoring and anomaly detection of HRS safety data, including pressure, temperature, and flow-rate measurements from compressors, storage tanks, and dispensers. The platform integrates data collection adapters, a time-series database, and machine learning-based diagnostic modules (regression, clustering, and classification) into a unified reference software framework. For anomaly detection, an unsupervised LSTM-Variational Autoencoder trained on normal operating data is combined with DBSCAN-based clustering and a Mann–Kendall trend test to jointly identify point anomalies and pattern-level drifts, addressing the scarcity of labeled abnormal data in HRS environments. A continual learning mechanism further adapts detection thresholds to gradual and abrupt pattern changes without full retraining. The system was deployed and validated at BAM’s demonstration hydrogen refueling station in Germany, integrated with a remote safety-monitoring system and confirmed through performance testing, demonstrating reliable, proactive hydrogen safety management.
Minsu Kim, Seongseop Kim, Seungwoo Lee et al.· Applied Sciences· 0 citations
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