Long‐Exposure Photogrammetry for Nighttime Measurement of Wind Turbine Blade Trajectory and Kinematics Parameters

To address the technical challenges of monitoring wind turbine blades under low‐light nighttime conditions, this study proposes a long‐exposure photogrammetry method based on stereo vision for monitoring wind turbine blades operating under low‐light conditions. This method uses frequency‐controllable LED markers. By considering parameters such as blade motion speed and distance, an appropriate frequency range is determined. Long‐exposure imaging is employed to capture the motion trajectory, and morphological operations, marker extraction, and computational solutions are applied to obtain the blade's trajectory and kinematic parameters. Two experiments were conducted to validate the method. First, the method was applied to measure a rotating blade in the laboratory. Compared with vibration analysis equipment, the errors in the first and second natural frequencies were −1.66% and 0.45%, respectively, demonstrating the method's ability to monitor blade motion trajectories and vibration characteristics. Second, under various motion conditions of the robotic arm, the proposed method yielded a trajectory error of 0.15% and a velocity error of −0.45% compared to the theoretical values. The results showed high consistency with the preset parameters of the robotic arm. This study provides a viable vision‐based solution for structural health monitoring of wind turbine blades, showing significant potential for operational monitoring in low‐light environments.