图像识别在特高压换流阀元件故障在线监测系统的应用

针对特高压换流阀元件众多、实时监测困难的问题，提出了一种基于图像识别技术的换流阀元件状态在线监测方法。介绍了图像数据提取及预处理的方法，分析了晶闸管门极线脱落及螺母力矩线偏移的图像特征，实现了晶闸管门极线脱落、螺母位移等故障的智能检测。基于图像识别及故障特征提取方法，设计了一套换流阀元件状态在线监测样机，详细介绍了样机的软件和硬件设计方法。在特高压直流输电阀塔上搭建试验环境，对图像分析和故障特征提取的方法进行测试，试验结果表明，所提出的图像分析和故障特征提取方法可有效识别换流阀元件的异常工况，对换流阀元件运行异常提前预警。

Application of Image Recognition in On-Line Monitoring System of UHVDC Valve Element Faults

UHVDC valve elements are numerous and hard to monitor in real time. An on-line monitoring method is thus proposed for converter valve element conditions based on image recognition technology. The paper introduces the method to extract and pre-process image data, and analyzes the image characteristics of thyristor gate wire shedding and nut torque line displacement. The image recognition technology is adopted to realize intelligent detection of faults such as thyristor gate wire shedding and nut displacement. A prototype is designed for on-line monitoring of converter valve element conditions based on the image recognition and fault feature extraction method, and its software and hardware design methods are introduced in detail. The testing environment is set up on a valve tower of UHVDC to test the image analysis and fault feature extraction method. The testing results show that the proposed method can effectively identify the abnormal working conditions of the valve elements, and realize the early warning of their abnormal operations, and can prevent the further expansion of faults.