基于大数据与线路画像的干线断线故障自适应判别方法  

An Adaptive Discrimination Method for Trunk Line Disconnection Fanlts Based on Big Data and Line Profiling

作　　者：姜忠炜 JIANG Zhongwei(State Grid Shizuishan Power Supply Company,Shizuishan,Ningxia 753000,China)

机构地区：[1]国网石嘴山供电公司,宁夏石嘴山753000

出　　处：《计算技术与自动化》2023年第4期159-163,共5页Computing Technology and Automation

摘　　要：针对干线断线故障特征无序化、整体判别过程自适应系数不稳定、判别误差增大的问题,引入大数据与线路画像两种算法,提出了一种干线断线故障自适应判别方法。分析干线断线故障点特征,构建线路画像,结合干线运行大数据,生成干线断线画像,改善判别方法自适应能力。分析低压位置的侧方位相电压关系,在现有画像结构中干线空闲位置上引入一组参量,判别通过变压器母线电压是否大于额定电压,二次利用大数据算法优化判别函数,自适应判别整体干线断线故障。仿真测试结果表明,该方法能够有效解决判别误差偏大问题,且整体适应性良好,满足实际应用要求。In view of the disordered characteristics of trunk line break fault,the unstable adaptive coefficient in the overall discrimination process and the increase of discrimination error,two algorithms,big data and line portrait,are introduced to propose an adaptive discrimination method for trunk line break fault.Analyze the characteristics of the broken line fault point of the trunk line,construct the line portrait,and generate the broken line portrait of the trunk line in combination with the big data of the trunk line operation,so as to improve the adaptive ability of the judgment method.Analyze the relationship between the side azimuth phase voltage at the low-voltage position,introduce a group of parameters to the idle position of the main line in the existing portrait structure,determine whether the bus voltage of the transformer is greater than the rated voltage,and use the big data algorithm to optimize the discriminant function twice to adaptively identify the overall main line disconnection fault.The simulation results show that this method can effectively solve the problem of large judgment error,and the overall adaptability is good,which meets the requirements of practical application.

关 键 词：大数据 线路画像 干线断线 判别函数 

分 类 号：U121[交通运输工程]