Zhang Le, Wei Jiadan
Wuxi Key Laboratory of Intelligent Robot and Special Equipment Technology, Wuxi Taihu University, Wuxi 214064, China.
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Sensors (Basel). 2024 Sep 13;24(18):5935. doi: 10.3390/s24185935.
This paper introduces a machine vision method for measuring the blade tip clearance in a wind turbine. An industrial personal computer (IPC) is installed in the nacelle of the wind turbine to continuously receive video data from a digital camera mounted at the bottom of the nacelle. Using the open-source computer vision (OpenCV) digital image processing library data base, the real-time trajectory of the turbine blades is determined from the video data. Furthermore, fast Fourier transform (FFT) analysis is performed for determining the operating frequency of the blades in the images. The amplitude analysis performed at this operating frequency reveals the pixel-based blade tip clearance, which is then used to calculate the actual clearance of the wind turbine. This value is subsequently transmitted to the main controller of the wind turbine. The main controller can enhance the operational safety of the wind turbine by implementing appropriate pitch control strategies to restrict and safeguard the blade tip clearance. The results obtained by conducting experiments on a 2.0 MW wind turbine unit validate the effectiveness of the proposed identification method. In this method, the blade tip clearance can be calculated effectively in real time, and both the video sampling rate and communication speed meet the requirements for controlling the blade pitch.
本文介绍了一种用于测量风力涡轮机叶片尖端间隙的机器视觉方法。在风力涡轮机的机舱中安装一台工业个人计算机（IPC），以持续接收安装在机舱底部的数字摄像机的视频数据。利用开源计算机视觉（OpenCV）数字图像处理库数据库，从视频数据中确定涡轮机叶片的实时轨迹。此外，执行快速傅里叶变换（FFT）分析以确定图像中叶片的运行频率。在此运行频率下进行的幅度分析揭示了基于像素的叶片尖端间隙，然后用于计算风力涡轮机的实际间隙。该值随后被传输到风力涡轮机的主控制器。主控制器可以通过实施适当的变桨控制策略来限制和保护叶片尖端间隙，从而提高风力涡轮机的运行安全性。在一台2.0兆瓦风力发电机组上进行实验所获得的结果验证了所提出识别方法的有效性。在该方法中，可以实时有效地计算叶片尖端间隙，并且视频采样率和通信速度均满足控制叶片变桨的要求。
