A Review of Advances in Composite Materials, Structural Optimization, and Machine Learning for Wind Turbine Blades: Challenges and Future Perspectives

ABSTRACT

This paper reviews recent advancements across the lifecycle of wind turbine blades, focusing on three interconnected areas: advanced composites, structural optimization, and machine learning (ML) diagnostics. In materials, we highlight progress in hybrid carbon/glass/natural fiber orientations, advanced manufacturing scalability, and microcapsule self‐healing matrices. In optimization, we evaluate multi‐objective evolutionary algorithms (e.g., NSGA‐II) that balance the conflict between aerodynamic efficiency and blade mass under strict deflection and fatigue constraints. In operational maintenance, we analyze deep learning frameworks (e.g., RNNs, Gaussian Processes) for real‐time structural health monitoring and predictive icing detection under environmental variations. The academic value of this review lies in bridging the gap between multi‐scale manufacturing defect physics and macro‐level AI diagnostics. Industrially, it provides a concise roadmap for wind energy engineers to optimize structural reliability, enhance manufacturing yields, and extend the lifespan of next‐generation megawatt‐scale blades.