摘要
电力线触树故障中,导线表面的遗留痕迹是事故防治和责任认定的重要依据,但目前中外针对触树后电力线放电痕迹特征规律和辨识方法的研究极其匮乏。为此,搭建10 kV中压线路触树放电实验平台,采集放电后的导线表面痕迹图像,并对导线表面痕迹特征进行系统分析,为人工巡检和智能化痕迹识别提供基础依据。然后,提出改进型Grabcut前景提取方法,综合利用U^(2)Net的自动分割特点和Grabcut的高精度优势,解决Grabcut算法中初始框无法自动确定的问题,实现复杂背景下导线痕迹区域自动精准分割。最后,提出基于低层纹理、颜色特征和高层深度特征的导线表面痕迹全面表征,并采用多数投票规则实现低层和高层特征识别结果决策融合,获得导线痕迹辨识结果,测试实验中平均识别准确率达到91.68%,证明了方法的有效性。
In power line contact tree faults,traces left on the power line surface are an important basis for accident prevention and responsibility determination.However,there is extremely limited research on the feature analysis and identification methods of discharge traces both domestically and internationally.To solve this problem,a 10 kV tree-line discharge experiment platform is built to collect power line surface trace images after discharge,and systematically analyze the characteristics of line surface traces,which provide a basic reference for manual inspection and intelligent trace recognition.Then,to solve the problem that the initial frame cannot be automatically determined in the Grabcut algorithm,an improved Grabcut foreground extraction method is proposed,which comprehensively uses the automatic segmentation characteristics of U^(2)Net and the high-precision advantages of Grabcut,to achieve automatic and accurate segmentation of line traces area under complex background.Finally,a comprehensive representation of power line surface traces is proposed based on texture,color feature at low level and deep feature at high level.A majority voting rule is adopted to achieve the decision fusion of the recognition results at low level and high level,and the recognition results of line traces are obtained.The average recognition accuracy rate reaches 91.68%in the test experiments,which proves the effectiveness of the proposed method.
作者
邹国锋
邵楠
王连辉
梁栋
徐丙垠
ZOU Guo-feng;SHAO Nan;WANG Lian-hui;LIANG Dong;XU Bing-yin(School of Electrical and Electronic Engineering,Shandong University of Technology,Zibo 255049,China;State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350003,China)
出处
《科学技术与工程》
北大核心
2024年第28期12239-12250,共12页
Science Technology and Engineering
基金
国家电网有限公司总部科技项目(5500-202221138A-1-1-ZN)。
关键词
树线放电
前景提取
低层特征
深度特征
决策融合
痕迹识别
Tree-line discharge
Foreground extraction
Low-level features
Deep features
Decision fusion
Trace recognition
