电力数据网中基于探针的故障定位算法

Xiaohan Gong, Kaixuan Wang, Shao-Yong Guo, Ao Xiong
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引用次数: 1

摘要

故障定位在电力数据网(EPDNet)中起着至关重要的作用。通常通过分析故障与症状之间的联系来实现。但是,许多症状可能无法与故障节点同步,并且在被动网络管理中无法检测到类型未知的潜在故障。在本文中,我们提出了一种基于主动探针的面向影响因子(FIF)算法。利用复杂网络理论,根据EPDNet中的所有业务,计算每个节点的影响因子。利用探针主动检测故障,以矩阵的形式实时显示网络状态,然后将影响因子应用到贝叶斯网络中,确定故障节点。仿真结果表明,该算法具有较高的准确率和较低的误报率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault location algorithm based on probe in Electric Power Data Network
Fault location plays a crucial role in Electric Power Data Network (EPDNet). It usually realized by analyzing the connection between failures and symptoms. However, many symptoms may not synchronize with faulty nodes and potential failures with unknown types remain undetected in passive network management. In this paper, we propose a Facing-the-Impact-Factor (FIF) algorithm using active approach based on probes. We use the complex network theory to calculate the impact factor of each node according to all services in EPDNet. And probes are applied to detect failures on initiative, thus a real time network state can be showed in the form of matrix, then apply the impact factor to Bayesian network to determine the faulty nodes. Simulation results demonstrate the high accuracy and low false positive rate of FIF algorithm.
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