Reliability Assessment and Condition Monitoring of Wind Energy Conversion Systems Using Bayesian Networks:Recent Advances and Key Insights

Wind energy conversion systems (WECSs) play a vital role in the transition to sustainable energy, requiring continuous advancements in reliability, efficiency, and predictive maintenance. This paper provides a comprehensive review of Bayesian Networks (BNs) as a robust probabilistic framework for enhancing fault detection, risk assessment, and condition monitoring in WECSs. By integrating diverse data sources—including supervisory control and data acquisition (SCADA) systems, sensor networks, and environmental monitoring tools—BNs facilitate predictive maintenance, improve failure diagnostics, and extend turbine lifespan through adaptive learning. Their capability to quantify uncertainty and model complex dependencies makes them particularly effective in addressing operational and failure uncertainties, ensuring reliable energy generation under variable environmental conditions. The paper further examines key implementation challenges, including computational demands, data integration complexities, and the need for high-quality datasets to refine probabilistic models. Future research directions focus on hybridizing BNs with deep learning (DL) and reinforcement learning (RL), incorporating real-time sensor data for adaptive reliability analysis, and developing scalable, computationally efficient frameworks. Given the rapid advancements in machine learning and data-driven methodologies over the past five years, this review highlights the evolving role of BNs in modernizing WECS operations, emphasizing the necessity of an updated perspective to address emerging challenges and opportunities in wind energy systems.