基于红外图像的避雷器故障检测方法

在电力系统中，利用计算机视觉和图像处理技术对避雷器进行故障检测，在保障电力系统的安全运行方面具有非常重要的作用。提出了一种基于红外图像的避雷器故障检测方法。该方法首先对输入图像进行预处理，利用尺度不变特征变换（Scale-invariant feature transform，SIFT）描述子和K-means++算法训练视觉字典精确定位避雷器，然后利用线性谱聚类对选择出的区域进行分割，最后通过分析避雷器热像的特征，实现避雷器故障的检测。实验结果说明所提出的算法可以有效地检测避雷器故障。

Abstract

In power systems, to use computer vision and image processing technologies to detect the faults in arresters plays an important role in their safe operation. An arrester fault detection method based on infrared images is proposed. The algorithm firstly preprocesses the input images and uses Scale-Invariant Feature Transform (SIFT) descriptors and K-means++ algorithm to train a vision dictionary to precisely position the arrester. Then, it uses Linear Spectral Clustering (LSC) to segment the area selected. Finally, it implements the detection of arrester fault by analyzing the characteristics in the thermal image of the arrester. The experimental results show that the proposed algorithm can detect the faults in arresters effectively.

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卢彬, 朱海峰, 谷振富, 高冠群, 李甲骏, 李世昌, 姚强. 基于红外图像的避雷器故障检测方法[J]. 红外, 2018, 39(1): 19. LU Bin, ZHU Hai-feng, GU Zhen-fu, GAO Guan-qun, LI Jia-jun, LI Shi-chang, YAO Qiang. Detection Method of Arrester Fault Based on Infrared Images[J]. INFRARED, 2018, 39(1): 19.

基于红外图像的避雷器故障检测方法

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