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Utilizing large-scale foundation models for prognostics and health management in wind turbines: Techniques, challenges, and future directions

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  • Yao, Jiachi
  • Han, Te

Abstract

The Paris Agreement has catalyzed global green development, accelerating progress in wind energy. In 2024, new global wind turbine installations totaled 117 GW, raising the cumulative capacity to 1136 GW. As wind turbine deployment rapidly expands, prognostics and health management (PHM) becomes essential for ensuring safe, reliable, and stable operations. Large-scale foundation models (LSF-Models), such as ChatGPT, hold significant potential for enhancing wind turbine PHM. However, research on LSF-Models in wind turbine PHM remains limited. This paper aims to bridge this gap by systematically analyzing recent advancements, technologies, challenges, and future directions in this area. It provides an in-depth review of wind turbine PHM methods and a comprehensive collection of publicly available datasets. The paper also outlines the core LSF-Model frameworks and representative models. Applications of LSF-Models in wind turbine PHM, such as condition monitoring and anomaly detection, fault diagnosis, remaining useful life prediction, and maintenance decision-making, are discussed. Additionally, the paper examines key technologies, including multimodal alignment and fusion, fine-tuning of LSF-Models, integration with local knowledge bases, and intelligent agent technologies. Finally, it addresses the challenges of applying LSF-Models to wind turbine PHM and suggests potential future research directions. In the era of artificial general intelligence, LSF-Models are poised to revolutionize wind turbine PHM, optimizing traditional methods and unlocking new development opportunities.

Suggested Citation

  • Yao, Jiachi & Han, Te, 2026. "Utilizing large-scale foundation models for prognostics and health management in wind turbines: Techniques, challenges, and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:rensus:v:227:y:2026:i:c:s1364032125012006
    DOI: 10.1016/j.rser.2025.116527
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