Review of Defect Detection Technology of Power Equipment Based on Video Images
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摘要: 基于视频图像的电力设备缺陷检测技术是实现电力智慧运维的关键技术之一，可解决电力设备故障自动诊断、主动预警和在线运维中存在的外部缺陷智能识别问题，减少人力资源浪费，提高电力系统巡检智能运维的频率与效率，从而弥补传统输变电设备巡检运维方式的不足。该文详细综述了当前典型的基于视频图像的输变电设备缺陷检测算法及图像处理技术，分析了传统图像处理方法及深度学习方法在电力设备缺陷检测领域应用的优缺点，总结了当前算法应用及开发平台的现状，指出了基于视频图像的输变电设备缺陷检测技术存在的问题，并展望了未来发展方向。Abstract: The defect detection technology of power equipment based on video image is one of the key technologies to realize intelligent operation and maintenance. It can solve the problems of intelligent identification of external defects in automatic fault diagnosis, active warning and online maintenance of power equipment. Moreover, it is able to reduce the waste of human resources and greatly improve the reliability of system operation and maintenance, thus making up for the shortcomings of traditional protection maintenance mode and providing technical support for the stable operation of power grid. This paper summarizes current typical defect detection algorithms and image processing technology of transmission and transformation equipment based on video images. Additionally, it analyzes the advantages and disadvantages of traditional image processing methods and deep learning methods in the field of power equipment defect detection. Finally, current algorithm development platforms are summarized, and the future development is predicted.
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表 1 输变电设备外部缺陷示例
设备 部件 缺陷 变压器
电抗器本体 渗漏油，金属锈蚀，油位计破损，部件外观变形，呼吸器破损，硅胶变色，油位、表计读数异常 端子箱 箱门闭合异常，金属锈蚀 套管 渗漏油、油位计破损、绝缘子破损、表面污秽、油位异常 冷却系统 渗漏油，金属锈蚀，部件外观变形，表面污秽 分接开关 渗漏油，金属锈蚀，油位计破损，呼吸器破损，硅胶变色，油位异常 构架及基础 金属锈蚀，异物 断路器 本体 异物悬挂，金属锈蚀，套管破损，渗漏油，分合闸指示破损，油位计破损，表面污秽，部件外观变形，呼吸器破损，硅胶变色，分合闸指示状态异常，油位状态异常，表计读数异常 操作机构 金属锈蚀，渗漏油，油位计破损，箱门闭合异常，部件外观变形，表计读数异常 隔离开关和接地开关 本体 异物悬挂，分合闸指示破损，瓷柱破损，支柱绝缘子破损，金属锈蚀，部件外观变形，
表面污秽，分合闸指示状态异常操作机构 异物悬挂，金属锈蚀，部件外观变形，箱门闭合异常 母线 母线导体 异物悬挂，金属锈蚀，导线破损，部件外观变形， 引流线 异物悬挂，导线破损，表面污秽 绝缘子串 异物悬挂，绝缘子破损，表面污秽 支柱绝缘子，构架及基础 异物悬挂，伞裙破损，金属锈蚀，表面污秽，外观变形 母线绝缘子 异物悬挂，瓷瓶破损，金属锈蚀，表面污秽 输电线路 绝缘子 金属锈蚀，污秽，均压环损伤，均压环脱落，均压环位移 杆塔 螺栓缺失，塔身锈蚀，异物 
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表 2 基于视频图像的输变电设备缺陷检测算法对比
任务 方法 优点 缺点 隔离开关 传统方法 能够实现分、合状态的自动识别 临界状态的识别准确率较低 深度学习方法 分、合、不到位3种状态识别准确 算法准确率依赖样本数据量 绝缘子破损 传统方法 模型简单，常规巡检准确率高 复杂拍摄背景下误差高 深度学习方法 在复杂拍摄背景下算法表现较好 训练需大量绝缘子破损图像数据 指针式仪表 传统方法 在指针数量较少、表盘刻度均匀且图像质量好时，具有较高的准确率 每种算法对应一种仪表，鲁棒性差 深度学习方法 同一个模型对应多种仪表，鲁棒性较强 训练所需数据量大 输电线路 传统方法 算法准确率较高 算法效果过分依赖拍摄清晰度 深度学习方法 算法成熟，同一模型对应多种缺陷 缺陷样本数据获取困难 温度检测 传统方法 测温精度及灵敏度高 测温区域边界模糊等问题 深度学习方法 测温区域自动标定，边帧界清晰 红外数据集少，获取困难
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