The recent increase in renewable energy penetration at the distribution level
introduces a multi-directional power flow that outdated traditional fault
location techniques. To this extent, the development of new methods is needed
to ensure fast and accurate fault localization and, hence, strengthen power
system reliability. This paper proposes a data-driven ground fault location
method for the power distribution system. An 11-bus 20 kV power system is
modeled in Matlab/Simulink to simulate ground faults. The faults are generated
at different locations and under various system operational states. Time-domain
faulted three-phase voltages at the system substation are then analyzed with
discrete wavelet transform. Statistical quantities of the processed data are
eventually used to train an Artificial Neural Network (ANN) to find a mapping
between computed voltage features and faults. Specifically, three ANNs allow
the prediction of faulted phase, faulted branch, and fault distance from the
system substation separately. According to the results, the method shows good
potential, with a total relative error of 0,4% for fault distance prediction.
The method is applied to datasets with unknown system states to test
robustness.

该研究提出了一种数据驱动的接地故障定位方法，通过分析处理后的数据的统计量，利用人工神经网络找到计算的电压特征与故障之间的映射关系，实现了接地故障的定位。研究结果表明该方法具有良好的潜力，并为测试鲁棒性而应用于未知系统状态的数据集。

分布式发电下配电系统基于数据驱动的接地故障定位方法

Data-Driven Ground-Fault Location Method in Distribution Power System  With Distributed Generation

This paper develops a novel graph convolutional network (GCN) framework for
fault location in power distribution networks. The proposed approach integrates
multiple measurements at different buses while taking system topology into
account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus
benchmark system. Simulation results show that the GCN model significantly
outperforms other widely-used machine learning schemes with very high fault
location accuracy. In addition, the proposed approach is robust to measurement
noise and data loss errors. Data visualization results of two competing neural
networks are presented to explore the mechanism of GCN's superior performance.
A data augmentation procedure is proposed to increase the robustness of the
model under various levels of noise and data loss errors. Further experiments
show that the model can adapt to topology changes of distribution networks and
perform well with a limited number of measured buses.

本文提出了一种新颖的图卷积网络（GCN）框架，用于电力配电网络的故障定位。该方法集成了不同母线上的多个测量值，并考虑了系统拓扑结构。在 IEEE 123 母线基准系统上，GCN 模型的有效性得到了证实。模拟结果表明，该模型的故障定位精度远高于其他广泛使用的机器学习方案。此外，该方法对于测量噪声和数据丢失错误具有鲁棒性。提出了数据增强过程来增加模型在各种噪声和数据丢失错误水平下的鲁棒性。进一步的实验表明，该模型能够适应配电网络的拓扑变化，并能在有限数量的测量母线下良好运行。