Abstract The topology structure of a distribution network is complex, and the amount of fault data is considerable, which induces strong fault data processing pressure on traditional cloud service platforms. Edge computing can address the distribution network autonomously and improve the efficiency of distribution network fault data processing. Therefore, in this paper, a novel method is proposed for line selection and fault location in a distribution network based on a cloud-edge-terminal hierarchical fault monitoring and control system. An edge computing gateway is installed at the front end of each main feeder. According to the transmission characteristics of the traveling wave-front, the time characteristic matrices before and after the fault are constructed, and the element variation rules of the matrices under different fault locations are analyzed. Line selection is realized in the cloud service platform; then, the fault location is accurately calculated at the edge computing gateway. To eliminate the calibration error of the initial traveling wave-front arrival time, a least squares model is introduced. The accuracy and reliability of line selection and fault location in the distribution network are improved by fitting the front and end terminal time characteristic matrices before and after the fault. The simulation analysis and field test results show that the proposed method is not affected by the line parameters or the topology structure, and it effectively eliminates the influence of the wave-front calibration error. The proposed method is simple to implement and has high accuracy, reliability and speed.

中文翻译：

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基于分层故障测控系统的配电网选线与故障定位方法

摘要 配电网拓扑结构复杂，故障数据量大，给传统云服务平台带来了较大的故障数据处理压力。边缘计算可以自主寻址配电网，提高配电网故障数据处理效率。因此，本文提出了一种基于云-边-端分级故障测控系统的配电网选线与故障定位新方法。每个主馈线的前端都安装了边缘计算网关。根据行波前的传输特性，构造故障前后的时间特征矩阵，分析了不同故障位置下矩阵的元素变化规律。在云服务平台中实现选线；然后在边缘计算网关准确计算故障位置。为了消除初始行波前到达时间的校准误差，引入了最小二乘模型。通过拟合故障前后的前端和终端时间特征矩阵，提高配网选线和故障定位的准确性和可靠性。仿真分析和现场测试结果表明，该方法不受线路参数或拓扑结构的影响，有效消除了波前标定误差的影响。该方法实现简单，准确率高，