Health Condition Monitoring, Intelligent Operation and Maintenance of Wind Turbines

Dear Colleagues,

With the continuous global growth in demand for clean energy, wind power generation, as a significant form of renewable energy generation, has been widely adopted. Wind turbine generators are typically installed in either terrestrial or marine environments. During long-term operation, due to the complex and harsh environments they are exposed to, various components of the units are prone to wear, fatigue, and other faults. In particular, the drive train system, as a critical link in energy transfer within wind turbine generators, directly impacts the units' power generation efficiency, operational reliability, and service life. Therefore, conducting research on the health condition monitoring and intelligent operation and maintenance (O&M) of wind turbine generators is of great significance for ensuring the stable operation of wind power generation systems, reducing O&M costs, and improving energy utilization efficiency.

This Special Issue focuses on the health condition monitoring and intelligent O&M of wind turbine generators. It aims to gather the latest research findings and advancements in relevant fields both domestically and internationally, facilitate academic exchanges, and promote the development and application of intelligent O&M technologies for wind turbines. The topics of interest for this Special Issue include, but are not limited to, the following:

• Intelligent sensing technologies;
• Advanced signal processing algorithms;
• Dynamic modeling and fault simulation of key components;
• Fault warning and identification of key components in wind turbine generators;
• Remaining useful life prediction based on deep learning;
• Digital twin-driven fault diagnosis of wind turbines;
• Knowledge graph and large-model technologies;
• Research on O&M technologies and modes for offshore wind power in deep and far sea areas;
• Predictive maintenance strategies for wind turbine generators.

Prof. Dr. Ronghua Zhu
Dr. Cailiang Zhang
Dr. Chaoge Wang
Dr. Zepeng Liu
Guest Editors

Manuscript Submission Information

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• wind turbines
• digital twin
• fault diagnosis
• predictive maintenance