基于改进区域全卷积网络的高压引线接头红外图像特征分析的在线故障诊断方法

Online Fault Diagnosis Method for Infrared Image Feature Analysis of High-Voltage Lead Connectors Based on Improved R-FCN

随着智能电网建设的不断发展,海量红外图像急剧增加,而传统红外故障检测依靠人工排查或手工提取特征,检测效率低且对人员经验依赖大。为实现对红外图像的高效智能化检测,保障电网安全运行,构建基于红外特征分析的在线故障诊断系统,提出通过改进高压引线接头红外图像的特征提取网络,以提升对小目标的识别性能,然后利用区域全卷积网络(R-FCN)实现对故障区域的定位和运行状态的识别,并且使用OpenCV对该故障区域的运行状态进行二次诊断,以进一步降低误报率。最后通过测试分析,改进后的R-FCN网络对高压引线接头红外图像故障诊断的平均精度达到了80.76%,比原R-FCN网络提升了8.43%。

With the continuous development of smart grid construction,massive infrared images have increased dramatically,while traditional infrared fault detection relies on manual inspection or manual extraction of features,low detection efficiency and great dependence on personnel experience.In order to realize the efficient and intelligent detection of infrared images and ensure the safe operation of the grid,this paper constructs an online fault diagnosis system based on infrared feature analysis,and proposes to improve the recognition performance of small targets by improving the feature extraction network of high-voltage lead connectors infrared images.Then,the region-based fully convolutional networks(R-FCN)is used to identify the location and operational status of the faulty area,and the operating state of the faulty area is secondarily diagnosed using OpenCV to further reduce the false alarm rate.Finally,through testing and analysis,the average accuracy of the improved R-FCN network for high-voltage lead connectors infrared image fault diagnosis reached 80.76%,which is 8.43%higher than the original R-FCN network.