基于门控循环注意力网络的配电网故障识别方法

doi:10.16183/j.cnki.jsjtu.2022.091

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (3): 295-303.doi: 10.16183/j.cnki.jsjtu.2022.091

• 新型电力系统与综合能源 • 上一篇下一篇

基于门控循环注意力网络的配电网故障识别方法

陈昊蓝¹, 靳冰莹¹, 刘亚东¹, 钱庆林², 王鹏³, 陈艳霞³, 于希娟³, 严英杰¹()

1.上海交通大学电子信息与电气工程学院,上海 200240
2.国网青海省电力公司,西宁 810008
3.国网北京市电力公司,北京 100031

收稿日期:2022-03-31 修回日期:2022-07-13 接受日期:2022-08-16 出版日期:2024-03-28 发布日期:2024-03-28
通讯作者: 严英杰,讲师;E-mail:yanyingjie@sjtu.edu.cn.
作者简介:陈昊蓝(2001-),本科生,主要从事配电网早期故障识别方法研究.
基金资助:
国家电网有限公司科技项目资助(52020121000C)

Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network

CHEN Haolan¹, JIN Bingying¹, LIU Yadong¹, QIAN Qinglin², WANG Peng³, CHEN Yanxia³, YU Xijuan³, YAN Yingjie¹()

1. School of Electronic Information and Electric Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. State Grid Qinghai Electric Power Company, Xining 810008, China
3. State Grid Beijing Electric Power Company, Beijing 100031, China

Received:2022-03-31 Revised:2022-07-13 Accepted:2022-08-16 Online:2024-03-28 Published:2024-03-28

摘要/Abstract

摘要：

为了提高小样本条件下配电网故障辨识准确率,提出一种门控循环注意力网络模型.首先,通过注意力机制赋予故障相中关键周期较高权重,通过加权运算使得模型更加关注上述关键信息.其次,利用门控循环网络处理波形序列,该网络利用门控信号控制记忆传递过程,并借由记忆传递建立序列中不同阶段输入波形和故障类别概率间的关系,从而提升识别准确率.基于仿真数据和实际数据的实验均表明:所提方法在小样本条件下的可靠性和准确率远优于同等条件下支持向量机、梯度提升决策树、卷积神经网络等常用分类模型,为配电网故障辨识技术提供了一种新思路.

关键词: 配电网, 故障辨识, 注意力机制, 门控循环网络

Abstract:

To improve fault identification accuracy in power distribution systems, a model named gated recurrent attention network is proposed. First, a higher weight is put on the key cycles of fault phase based on the attention mechanism, making the model focus more on these key messages by weight assignment. Then, the gated recurrent network is adopted, which controls the memory transmission with gate signal and constructs the relationship between input waveform and probability of events at different stages to process the waveform sequence, thereby improving recognition accuracy. Experiments based on both simulation and field data show that the proposed method, under the small-sample-learning condition, is much better than other commonly-used classification models, such as support vector machine, gradient boosting decision tree, and convolutional neural network, providing new insights into fault identification technology in power distribution systems.

Key words: power distribution systems, faults identification, attention mechanism, gated recurrent units (GRU)

中图分类号:

TM726

陈昊蓝, 靳冰莹, 刘亚东, 钱庆林, 王鹏, 陈艳霞, 于希娟, 严英杰. 基于门控循环注意力网络的配电网故障识别方法[J]. 上海交通大学学报, 2024, 58(3): 295-303.

CHEN Haolan, JIN Bingying, LIU Yadong, QIAN Qinglin, WANG Peng, CHEN Yanxia, YU Xijuan, YAN Yingjie. Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network[J]. Journal of Shanghai Jiao Tong University, 2024, 58(3): 295-303.

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模型	仿真类别						平均值
模型	1		2		3		平均值
GRU	0.902		0.941		0.887		0.910
SVM	0.810		0.841		0.815		0.822
GBDT	0.832		0.869		0.844		0.848
CNN	0.871		0.890		0.867		0.876
模型	实验类别						平均值
模型	1	2		3		4	平均值
GRU	0.862	0.871		0.854		0.831	0.855
SVM	0.720	0.704		0.731		0.719	0.719
GBDT	0.762	0.774		0.785		0.747	0.767
CNN	0.790	0.807		0.820		0.787	0.801

模型	仿真类别						平均值
模型	1		2		3		平均值
GRAN	0.940		0.977		0.930		0.949
GRU	0.902		0.941		0.887		0.910
模型	实验类别						平均值
模型	1	2		3		4	平均值
GRAN	0.921	0.940		0.901		0.897	0.915
GRU	0.862	0.871		0.854		0.831	0.855

基于门控循环注意力网络的配电网故障识别方法

Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network

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