基于多维故障特征提取的CNN-BiGRU-ATT多分支配电网故障定位

张玉敏; 王德龙; 张晓; 吉兴全; 张祥星; 黄心月; 王学林

doi:10.11930/j.issn.1004-9649.202507050

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基于多维故障特征提取的CNN-BiGRU-ATT多分支配电网故障定位

新型电网 | 更新时间：2026-02-03

- 基于多维故障特征提取的CNN-BiGRU-ATT多分支配电网故障定位
- CNN-BiGRU-ATT multi-branched distribution network fault location based on multi-dimensional fault feature extraction
- 中国电力 2026年59卷第1期页码：163-174
- 作者机构：
  
  1. 山东科技大学电气与自动化工程学院,山东,青岛,266590
  2. 国网山东省电力公司德州市陵城区供电公司,山东,德州,253500
  3. 青岛科技大学自动化与电子工程学院,山东,青岛,266061
- 作者简介：
- 基金信息：
  
  国家自然科学基金青年资助项目（52107111）;中国博士后面上资助项目（2023M734092）;山东省自然科学基金资助项目（ZR2022ME219，ZR2023QE181，ZR2024ME029）。
- DOI：10.11930/j.issn.1004-9649.202507050
  中图分类号：
- 网络出版：2026-01-13，
  
  纸质出版：2026-01-13
- 稿件说明：
移动端阅览
张玉敏, 王德龙, 张晓, 吉兴全, 张祥星, 黄心月, 王学林. 基于多维故障特征提取的CNN-BiGRU-ATT多分支配电网故障定位[J]. 中国电力, 2026, 59(1): 163-174.

张玉敏, 王德龙, 张晓, et al. CNN-BiGRU-ATT multi-branched distribution network fault location based on multi-dimensional fault feature extraction[J]. 2026, 59(1): 163-174.
张玉敏, 王德龙, 张晓, 吉兴全, 张祥星, 黄心月, 王学林. 基于多维故障特征提取的CNN-BiGRU-ATT多分支配电网故障定位[J]. 中国电力, 2026, 59(1): 163-174. DOI： 10.11930/j.issn.1004-9649.202507050.

张玉敏, 王德龙, 张晓, et al. CNN-BiGRU-ATT multi-branched distribution network fault location based on multi-dimensional fault feature extraction[J]. 2026, 59(1): 163-174. DOI： 10.11930/j.issn.1004-9649.202507050.

摘要

针对多分支配电网故障定位在微弱故障条件下故障特征提取困难的问题，提出了基于多维故障特征提取的卷积神经网络（convolution neural network，CNN）-双向门控循环单元（bidirectional gated recurrent unit，BiGRU）-注意力机制（attention mechanism，ATT）多分支配电网故障定位方法。首先，分析不同故障位置和故障分支的行波特性，采用基于直线检测（line segment detector，LSD）的波头标定方法提取故障波头的坐标、幅值和斜率等信息，利用主成分分析法（principal component analysis，PCA）构造与故障位置成映射关系的多维故障特征空间；其次，构建CNN-BiGRU-ATT故障定位模型，深入挖掘时序特征和幅值特征与故障位置之间的关联；最后，结合分类与回归任务，分别实现故障区段定位与精准定位。在有限样本的情况下，区段定位准确率达99.6429%，精准定位误差55.77 m，跨工况误差最低2.95 m。结果表明，该模型能有效关联多维故障特征与故障信息，较对比模型具有更优的故障定位精度稳定性与场景泛化能力。

Abstract

To address the difficulty in fault feature extraction for multi-branched distribution network fault location under weak fault conditions

this paper proposes a fault location method based on multi-dimensional feature extraction

which integrates the convolutional neural network (CNN)

bidirectional gated recurrent unit (BiGRU)

and attention mechanism (ATT). Firstly

the traveling wave characteristics of different fault locations and fault branches are analyzed. A wavefront calibration method based on the line segment detector (LSD) is employed to extract information features such as the coordinates

amplitudes

and slope of fault wavefronts

and principal component analysis (PCA) is used to construct a multi-dimensional fault-feature space that maps to fault locations. Then

a CNN-BiGRU-ATT fault location model is established to deeply explore the correlations between temporal features

amplitude features

and fault locations. Finally

classification and regression tasks are integrated to achieve both fault section identification and precise fault location. Under the condition of limited samples

the fault section location accuracy reaches 99.6429%

the accurate location error is 55.77 m

and the cross-condition error is as low as 2.95 m. The results show that the proposed model can effectively correlate multi-dimensional fault features with fault information

and exhibits superior stability of fault location accuracy and scenario generalization ability compared with the comparative models.

关键词

Keywords

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