Artificial intelligence-based design optimization for wind turbines: A comprehensive review of its methodologies, applications, and challenges

Although use of wind energy has increased significantly in the recent years, the design optimization of wind turbines (WTs) remains hindered by challenges such as computational efficiency, multi-objective trade-offs, and system complexity. While the rapid evolution of artificial intelligence (AI) has offers innovative solutions for aerodynamic design, structural optimization, and control systems, a systematic synthesis on how AI paradigms address these cross-domain challenges in WT design optimization is lacking. To bridge this gap, this review provides a comprehensive survey of AI-based design optimization methodologies for WTs, categorizing into supervised learning-based methods, unsupervised learning-based methods, and reinforcement learning-based methods. These methods are systematically mapped to specific design optimization challenges across key WT subsystems, including blades, towers, and generators. Through a critical examination of 177 related publications, this review demonstrates AI's transformative impact on the design optimization methods for WTs, and identifies the critical challenges of the methods including data scarcity for novel designs, interpretability of black-box models, and integration of multi-physics constraints, etc. Finally, future work has been proposed to emphasize hybrid digital twin frameworks, federated learning for distributed optimization, and quantum-inspired algorithms for large-scale WT design optimization problems.