The rapid expansion of wind power capacity has highlighted the limitations of traditional operation and maintenance (O&M) models, which are plagued by high costs, low efficiency, and significant safety risks. Multimodal large AI models offer a transformative solution for intelligent O&M transformation in the wind power industry. This paper provides a systematic review of multimodal large models and their applications within the sector. It first outlines the characteristics of multimodal data in wind power equipment—such as Supervisory Control And Data Acquisition(SCADA) system data, vibration signals, infrared images, acoustic signals, and text reports—along with key fusion processing technologies. Then, it examines the current applications, technical frameworks, and future challenges of large models in domains including wind turbine fault diagnosis, predictive maintenance, and repair decision support. This review serves as a reference for understanding and advancing the in-depth application of multimodal artificial intelligence technology in wind power.

Multimodal large models, artificial intelligence; wind power operation and maintenance; fault diagnosis; predictive maintenance; condition monitoring