With the advantages of clean, renewable energy, offshore wind power has become one of the fastest-growing energy sources and one of the most economical solutions for electricity generation. Consequently, the number and scale of offshore wind farms have been developing rapidly. In light of climate change, demand continues to grow. However, to address challenges related to availability, production, and cost reduction for wind farm owners, it is critically urgent to investigate and explore advanced intelligent techniques for the monitoring, operation, and maintenance of offshore wind farms. These techniques are essential for improving the safety and reliability of wind farm systems, reducing operation and maintenance costs, enhancing power generation efficiency, and accelerating the implementation of offshore wind farms.

Advanced techniques have made tremendous progress in the monitoring, operation, and maintenance of offshore wind farms. However, current approaches are often fully data-driven, making it difficult to handle the massive amounts of monitoring and detection data effectively and incorporating limited domain knowledge in areas such as data processing, fault diagnosis, and fault prediction. Additionally, robust manufacturing technologies and continuous data monitoring for early warning in these safety-critical power assets often result in complex fault mechanisms and fewer fault samples for key components of offshore wind farms. Consequently, current methods frequently exhibit poor generalization performance and provide unreliable diagnostic and predictive decisions for wind farm owners.

In this context, it is significantly necessary to explore and examine advanced and innovative paradigms for data processing, condition monitoring, fault diagnosis, fault prediction, and maintenance decision-making. These paradigms have the potential to substantially enhance the flexibility and reliability of advanced techniques, thereby improving their application in the monitoring, operation, and maintenance of offshore wind farms.

The primary objective of this Research Topic is to present collective insights and novel methodologies related to advanced techniques for monitoring, operation, and maintenance, aimed at enhancing the high reliability and low maintenance costs of offshore wind farms. We welcome a variety of article types, including Original Research, Reviews, and Perspectives. Contributions are sought on the following topics, but are not limited to:

• Advanced techniques for monitoring offshore wind farms
• Advanced techniques for the operation and maintenance of wind farms
• Trustworthy decision-making support for the operation and maintenance of wind farms
• Advanced techniques for fault diagnosis of wind farms
• Advanced techniques for wind power forecasting
• Integrated physical models and data-driven methods for wind farm management
• Innovative inspection technologies for offshore wind farms

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Frontiers in Energy Research

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