Health prognostics and maintenance decision-making for wind energy: A comprehensive overview

Abstract

As wind power installations continue to expand rapidly, ensuring reliable and cost-effective Operation and Maintenance (O&M) over the wind turbine lifetime has become increasingly important. With the development of Industry 4.0, predicting the health status of wind turbines and making informed maintenance decisions has become an urgent challenge that must be addressed to enable the next generation of O&M paradigms. This paper starts with presenting a comprehensive review of health prognostics for wind turbines. Existing approaches are generally divided into two main categories: (1) model-based methods, including physics-based and knowledge-based approaches, and (2) data-driven methods, which encompass statistical methods as well as Artificial Intelligence (AI)-based methods, including both traditional and emerging AI methods. Subsequently, the maintenance decision-making problem informed by wind turbine health information is systematically summarized, with a particular focus on the historical evolution, problem formulation, data challenges, modeling techniques, optimization objectives, and solving techniques. Finally, key open challenges in the context of future digital and intelligent O&M are highlighted, and potential research directions are outlined to address these challenges.