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Single-phase Grounding Fault Location Method for Distribution Network Based on Convolutional Neural Network

机译：基于卷积神经网络的配电网单相接地故障定位方法

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When a single-phase ground fault occurs in the distribution network, it is generally allowed to operate with faults from 1 to 2 hours, which may lead to further development of the fault and even threaten the safe operation of the power system. Therefore, when a small current system has a ground fault, it must be quickly diagnosed to shorten the time for the distribution network operation and maintenance personnel to eliminate the fault, thereby improving the safety of the distribution network operation. It proposes a single-phase grounding fault line selection method based on convolutional neural network (CNN). Phasor Measurement Unit (PMU) is used to extract the negative sequence voltage and current phasors at both ends of the fault line, and the principle of double-terminal fault location is applied to high-precision fault location. Simulink simulation results show that the proposed method can realize line selection and accurate location of single-phase ground fault in distribution network, and is not affected by system frequency, fault location, transition resistance or other factors.
机译：当配电网络中发生单相接地故障时，通常允许其运行1到2个小时的故障，这可能导致故障的进一步发展，甚至威胁到电力系统的安全运行。因此，当小电流系统发生接地故障时，必须迅速进行诊断，以缩短配电网运维人员排除故障的时间，从而提高配电网运行的安全性。提出了基于卷积神经网络的单相接地故障选线方法。相量测量单元（PMU）用于提取故障线路两端的负序电压和电流相量，并将双端故障定位原理应用于高精度故障定位。 Simulink仿真结果表明，该方法可以实现配电网中单相接地故障的选线和准确定位，不受系统频率，故障位置，过渡电阻等因素的影响。

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