Machine Learning for Condition Monitoring of Wind Energy Systems

Dear Colleagues,

With the advent of machine learning (ML) and artificial intelligence (AI), there is unprecedented potential to transform condition monitoring, enabling real-time diagnostics, predictive maintenance, and anomaly detection in wind energy systems. This Special Issue invites researchers and practitioners to present recent advances and innovative applications of AI and ML techniques for the condition monitoring of wind energy systems. Topics of interest include, but are not limited to, the following:

• AI and ML models including deep learning, ensemble methods, and transfer learning for fault detection and diagnosis in wind farms.
• Predictive maintenance frameworks using AI and ML models to predict failures and optimize maintenance schedules.
• Novel approaches for identifying anomalous behavior in wind turbines, to prevent unexpected failures.
• Data fusion techniques for multiple sensors, such as vibration, temperature, acoustic, and SCADA.
• Explainability and interpretability of AI and ML models in condition monitoring.
• Applications of edge-based ML solutions for real-time condition monitoring in remote wind farms.
• Data challenges in condition monitoring.

Prof. Dr. Mahmood Shafiee
Dr. Demetrio Cornilios Zachariadis
Guest Editors

Manuscript Submission Information

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