基于MST框架的PCNN输电线路红外热故障区域提取方法  

PCNN Infrared Fault Region Detection Along Transmission Lines Based on the MST Framework

作　　者：葛黄徐 郑雷[2,3] 江洪 郭一凡 周东国 GE Huangxu;ZHENG Lei;JIANG Hong;GUO Yifan;ZHOU Dongguo(State Grid Zhejiang Electric Power Company Jiaxing Power Supply Company,Jiaxing 314599,China;Wuhan NARI Limited Liability Company of State Grid Electric Power Research Institute,Wuhan 430074,China;NARI Technology Co.Ltd.,Nanjing 211106,China;Wuhan University,The College of Power and Mechanical Engineering,Wuhan 430072,China)

机构地区：[1]国网浙江省电力有限公司嘉兴供电公司,浙江嘉兴314599 [2]国网电力科学研究院武汉南瑞有限责任公司,湖北武汉430074 [3]南瑞集团有限公司,江苏南京211106 [4]武汉大学电气与自动化学院,湖北武汉430072

出　　处：《红外技术》2022年第7期709-715,共7页Infrared Technology

基　　金：国家电网公司总部科技项目(521104180025)。

摘　　要：针对输电线路电气设备红外热故障检测,提出采用一种基于最大相似度阈值(Maximum Similarity Thresholding, MST)的脉冲耦合神经网络(Pulse-coupled neural Network, PCNN)红外图像热故障区域提取方法。在该方法中,利用脉冲耦合神经元对相似的邻域神经元同步点火特性,通过引入最大相似度阈值框架,简化了PCNN模型的阈值设置机制。同时,针对相似邻域神经元的同步点火特性,采用最小聚类方差设置连接系数,使得PCNN模型在自适应迭代下最终获取热故障区域。最后通过真实输电线路电气设备红外故障图像测试,验证了文中所提方法的有效性和适用性,为PCNN模型的推广应用奠定了基础。This paper presents a pulse-coupled neural network(PCNN) method for infrared fault region extraction based on maximum similarity thresholding to detect the fault region from the infrared image of a transmission line. In this method, the synchronous pulse characteristics of the PCNN model are used to cluster pixels via inner iteration, and the model is simplified by incorporating the maximum similarity thresholding method, enabling the PCNN model to simplify the thresholding setting. Meanwhile, the minimum clustering variance is introduced to set the linking coefficient. Thus, the PCNN model can efficiently segment an infrared image and obtain the effective thermal fault region in the image. The experimental results show that the proposed method exhibits good performance in region extraction and may be suitable for increasing the efficiency of automatic fault detection along transmission lines.

关 键 词：MST框架 脉冲耦合神经网络 输电线路 红外图像 聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]