Fault Line Selection Method of Small Current Grounding System Based on Deep Learning

Guodong ZHANG; Haitao PU; Kai LIU

doi:10.12096/j.2096-4528.pgt.19103

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Fault Line Selection Method of Small Current Grounding System Based on Deep Learning

Energy Internet | 更新时间：2025-07-04

- Fault Line Selection Method of Small Current Grounding System Based on Deep Learning
- Power Generation Technology Vol. 40, Issue 6, Pages: 548-554(2019)
- 作者机构：
  
  1.山东科技大学电气信息系, 山东省济南市 253500
  2.洛阳供电公司, 河南省洛阳市 471000
- 作者简介：
- 基金信息：
  
  Ministry of Education's Cooperative Education Program Project(201702064021);Research Project of Jinan Campus of Shandong University of Science and Technology(JNJG2017203)
- DOI：10.12096/j.2096-4528.pgt.19103
  CLC：
- Received：06 July 2019，
  
  Published：30 December 2019
- 稿件说明：
移动端阅览
Guodong ZHANG, Haitao PU, Kai LIU. Fault Line Selection Method of Small Current Grounding System Based on Deep Learning[J]. Power Generation Technology, 2019, 40(6): 548-554.
DOI：

Guodong ZHANG, Haitao PU, Kai LIU. Fault Line Selection Method of Small Current Grounding System Based on Deep Learning[J]. Power Generation Technology, 2019, 40(6): 548-554. DOI： 10.12096/j.2096-4528.pgt.19103.

摘要

小电流接地系统的单相接地故障选线问题目前仍没有完全解决。为了提高单相接地故障选线成功率，提出一种基于深度学习网络的选线方法。首先，利用PSCAD搭建了中性点不接地系统仿真模型，通过设置每条线路在不同接地电阻下的故障，得到基于各出线零序电流幅值和相角的样本数据，并将样本数据分为训练集、验证集和测试集3部分。其次，基于Keras搭建了深度学习神经网络，利用训练集和验证集数据对该网络进行训练。最后，利用测试集数据对训练好的模型进行测试。结果表明，该方法具有建模简单、成功率高及选线不受过渡电阻影响的特点。

Abstract

At present

the single-phase grounding fault line selection problem of small current grounding system has not been completely solved. In order to improve the success rate of single-phase grounding fault line selection

a new method based on deep learning network was proposed. Firstly

the simulation model of neutral un-grounded system was built by PSCAD. By setting the fault of each line under different grounding resistors

the sample data based on the zero sequence current value and phase angle of each line was obtained out. The sample data was divided into three parts:training set

verification set

and test set. Secondly

a deep learning neural network was constructed based on Keras

and the network was trained using the training set and validation set. Finally

the trained model was tested The results show that the method has the characteristics of simple modeling

high success rate and no influence of transition resistance.

关键词

Keywords

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