Visualization of Wind Turbine Status Based on AR Technology

This study aims to explore a state visualization method for wind turbines based on AR technology, with the aim of improving the monitoring and maintenance efficiency of wind turbines. By optimizing the ORB-PCA feature point extraction algorithm, the accuracy and robustness of feature point matching have been significantly improved, laying the foundation for accurate registration of AR technology in complex wind farm environments. By combining the Tracking Learning Detection (TLD) algorithm with ORB-PCA feature points, efficient and stable target tracking has been achieved, ensuring real-time performance of the system. At the same time, the imaging principle of the camera was deeply studied, and the correspondence between virtual information and the real world was accurately constructed through camera calibration and homography matrix calculation. In terms of system implementation, a complete AR visualization system integrating feature registration, information presentation, and interaction functions has been constructed, which realizes the intuitive display of three-dimensional models and dynamic data of wind turbine status, and improves the cognitive ability and work efficiency of operation and maintenance personnel. This study provides strong technical support for the intelligent monitoring and maintenance of wind turbines, and has broad application prospects.