Comparison and analysis of major research methods for non-destructive testing of wind turbine blades

Since the establishment of global goals for carbon neutrality and peak carbon emissions, optimizing renewable energy use has become a global priority. Wind turbine blades, as core components of wind power systems, require effective health monitoring and damage identification to ensure stable turbine operation and enhance economic efficiency. This paper applies bibliometric analysis to classify existing blade damage detection methods, comparing major non-destructive testing techniques, including strain data monitoring, vibration data monitoring, acoustic measurement, ultrasonic testing, thermal imaging, and image recognition. This paper discusses the application scenarios, strengths, and limitations of each technique, with an emphasis on future trends, and includes damage assessment through multi-method integration, advancements in online and non-destructive damage detection technologies, and the application of intelligent algorithms, such as deep learning. This study aims to guide wind power professionals in selecting blade health monitoring technologies, thereby promoting sustainability and efficiency in the wind power industry.