改进小波基与分尺度跨线路注意力融合的配电网高阻接地故障辨识方法

陈春; 张钰祥; 曹一家; 安义; 钟俊杰; 苏译

doi:10.16383/j.aas.c260127

改进小波基与分尺度跨线路注意力融合的配电网高阻接地故障辨识方法

doi: 10.16383/j.aas.c260127 cstr: 32138.14.j.aas.c260127

陈春^1,,
张钰祥^1,,
曹一家^1,,
安义^2,,
钟俊杰^1,,
苏译^3,

1.
长沙理工大学电网防灾减灾全国重点实验室长沙 410114
2.
国网江西省电力有限公司电力科学研究院南昌 330096
3.
湘潭大学自动化与电子信息学院湘潭 411105

基金项目: 国家自然科学基金(52577081), 湖南省自然科学基金(2023JJ20039)资助

详细信息

作者简介:
陈春：长沙理工大学副教授. 主要研究方向为智能配电网规划与自愈控制. 本文通信作者. E-mail: chch3266@126.com

张钰祥：长沙理工大学硕士研究生. 主要研究方向为配电网故障辨识. E-mail: 24105011053@csust.edu.cn

曹一家：长沙理工大学教授. 主要研究方向为电力系统安全与控制, 大电网智能优化调度. E-mail: yjcao@csust.edu.cn

安义：国网江西省电力有限公司电力科学研究院高级工程师, 主要研究方向为配电变压器安全经济运行, 配电线路故障诊断. E-mail: nandeanyi@163.com

钟俊杰：长沙理工大学讲师. 主要研究方向为综合能源系统优化与安全分析. E-mail: zhongii@csust.edu.cn

苏译：湘潭大学讲师. 主要研究方向为知识与数据驱动的电力系统优化调度. E-mail: suyi2018@xtu.edu.cn

中图分类号: Y
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- 被引次数: 0
出版历程
- 收稿日期: 2026-02-24
- 录用日期: 2026-06-02
- 网络出版日期: 2026-07-20

An Improved High-impedance Grounding Fault Identification Method for Distribution Networks via an Enhanced Wavelet Basis and Multi-Scale Cross-line Attention Fusion

CHEN Chun^1
,,
ZHANG Yu-Xiang^1
,,
CAO Yi-Jia^1
,,
AN Yi^2
,,
ZHONG Jun-Jie^1
,,
SU Yi^3
,

1.
State Key Laboratory of Disaster Prevention & Reduction for Power Grid, Changsha University of Science & Technology, Changsha 410114
2.
Electric Power Research Institute, State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096
3.
Sc-hool of Automation and Electronic Information, Xiangtan University, Xiangtan 411105

Funds: Supported by National Natural Science Foundation of China (52577081) and Hunan Provincial Natural Science Foundation (2023JJ20039)

More Information

Author Bio:
CHEN Chun　Associate professor at Changsha University of Science & Technology. His research interests include smart power distribution network planning and self-healing control. Corresponding author of this paper

ZHANG Yu-Xiang　Master student at Changsha University of Science & Technology. His research interest include power distribution network fault identification

CAO Yi-Jia　Professor at Changsha University of Science & nology. His research interests include power system sccurityand controland intelligent optimal dispatching of large-scale power grids

AN Yi　Senior cnginecr at the Electric Power Rescarch Institute, State Grid Jiangxi Eltric Power Co., Ltd.. His research interests include safe and economic operation of distribution transformers and fault diagnosis of distribution lines

ZHONG Jun-Jic　Lecturer at Changsha University of Science & Technology. His research interests include optimization and security analysis of integrated energy systems

SU Yi　Lecturer at Xiangtan University. His research interests include knowledge and data-driven optimal dispatching of powersystems

摘要

摘要: 配电网高阻接地故障下, 故障初始电流微弱且常伴有非线性电弧, 暂态特征微弱, 短时内故障难以可靠辨识, 存在电气火灾与人身触电事故风险. 为此, 提出改进小波基与分尺度跨线路注意力融合的配电网高阻接地故障辨识方法. 建立分尺度特征跨线路辨识模型, 利用汉明窗滤波器抑制频谱泄漏, 提取具有高物理显著性的小波关键系数, 提升故障特征在信号中的对比度; 引入尺度内编码机制, 将长序列特征按尺度等效降维, 形成低维度嵌入向量; 构建分尺度跨线处理模型, 改进注意力机制, 显式捕捉故障线路与健全线路间的差异性, 提高配电网高阻接地故障的辨识准确率. 仿真实验表明, 所提方法相较其他模型, 高阻接地故障辨识准确率最高提升36.6%;动模实验和真型实验表明, 即使面对训练阶段未出现的新拓扑, 所提方法在真实链路情况下的辨识准确率仍超95%, 验证了该方法在真实工况环境下的适用性与一定的鲁棒性.
- 高阻接地故障 /
- 配电网故障 /
- 故障选线 /
- 深度学习 /
- 注意力机制 /
- 小波变换
Abstract: Under high-impedance grounding faults (HIGF) in power distribution networks, the initial fault current is extremely weak and often accompanied by nonlinear arcing. Because these transient signatures exhibit low energy and poor contrast within short observation windows, achieving reliable faulted-line selection and identification remains challenging, which in turn increases the risks of electrical fires and electric shock accidents. To address this issue, this paper proposes an improved method that fuses an enhanced wavelet basis with multi-scale cross-line attention. First, a multi-scale feature extraction and cross-line identification model is established, where Hamming window filter is used to mitigate spectral leakage and wavelet key coefficients with high physical salience are extracted to enhance the contrast of fault features in measured signals. Next, a scale-wise encoding mechanism is introduced to compress long-sequence features into low-dimensional embedding for each scale. Furthermore, a multi-scale cross-line attention module is constructed by refining the attention mechanism to explicitly capture the disparities between the faulty line and healthy lines across scales, thereby improving the accuracy and robustness of HIGF identification. Simulation results demonstrate that the proposed method improves the identification accuracy by up to 36.6% compared to other models. Moreover, dynamic model experiments and real prototype experiments indicate that the proposed method maintains an identification accuracy above 95% under real-world link conditions, even when encountering network topologies not encountered during training, thereby validating its applicability and robustness in practical operating environments.
- high-impedance grounding fault /
- power distribution network fault /
- fault line selection /
- deep learning /
- attention mechanism /
- wavelet transform

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图 1 改进小波基与分尺度跨线路注意力融合的接地故障辨识方法总体框架

Fig. 1 The overall framework of the proposed grounding fault identification method integrating improved wavelet basis with multi-scale cross-Line attention

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图 2 高阻接地故障辨识流程图

Fig. 2 Flowchart of high-impedance grounding fault identification

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图 3 10 kV配电网仿真模型

Fig. 3 10 kV power distribution network simulation model

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图 4 各关键尺度泄漏比分布对比

Fig. 4 Comparative distribution of leakage ratios at different key scales

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图 5 各方法对比度与泄漏比柱状图

Fig. 5 Comparison of contrast and leakage ratio among various methods

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图 6 不同前端方法训练损失性能对比

Fig. 6 Performance comparison of training loss for various front-end methods

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图 7 不同前端方法在噪声条件下的性能下降对比

Fig. 7 Performance degradation comparison of different front-end methods under noise conditions

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图 8 不同模型训练损失性能对比

Fig. 8 Training loss performance comparison of various models

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图 9 不同后端方法在噪声条件下性能下降对比

Fig. 9 Impact of noise on the performance stability of different back-end methods

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图 10 消融实验结果对比

Fig. 10 Comparison of ablation experiment results

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图 11 10 kV动模实验系统拓扑

Fig. 11 System topology of the 10 kV dynamic simulation experiment

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图 12 10 kV动模实验系统平台现场

Fig. 12 On-site view of the 10 kV dynamic model experiment system platform

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图 13 高阻接地实验现场

Fig. 13 On-site view of the high-impedance grounding experimental scenario

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图 14 树线放电实验现场

Fig. 14 On-site view of the tree-contact discharge experimental scenario

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图 15 真型实验系统拓扑

Fig. 15 Topology of the physical prototype experimental system

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图 16 真型实验系统现场图

Fig. 16 Field photograph of the physical prototype experimental system

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表 1 仿真与训练参数设置

Table 1 Simulation and training parameter settings

参数项	参数
系统电压等级	10 kV
出线数量	4条
线路长度范围	6 ~ 8 km
故障类型	单相接地故障
故障相别	A/B/C
样本	5000
采样率	12800 Hz
窗口长度	2560
学习率	$1 \times 10^{-4}$
批次大小	32
丢弃率	0.2
注意力头数	4
嵌入维度	64

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表 2 对照条件与固定参数设置

Table 2 Control conditions and fixed parameter settings

参数项	参数
样本	50组
窗口大小	2560点
对比窗口	故障后1/4周波
分解层数	4层
关键尺度	$\{cA_4,\; cD_4,\; cD_3\}$

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表 3 对照条件与固定参数设置

Table 3 Control conditions and fixed parameter settings

参数项	参数
样本数	5000组
窗口大小	2560点
分解层数	4层
关键尺度	$\{cA_4,\; cD_4,\; cD_3\}$
训练轮次	100
早停轮次	15

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表 4 不同前端方法在噪声条件下性能的指标

Table 4 Performance metrics of different front-end methods under noisy conditions

方法	Top-1Acc (%)			Macro-F1 (%)			Macro-Recall (%)
SNR (dB)	$ \infty $	10	0	$ \infty $	10	0	$ \infty $	10	0
汉明窗	100.0	100.0	98.8	100.0	100.0	98.5	100.0	100.0	98.8
db4	100.0	100.0	77.2	100.0	100.0	76.9	100.0	100.0	76.8
sym8	100.0	92.4	47.6	100.0	92.4	47.4	100.0	92.4	47.4

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表 5 不同前端方法的泛化性能指标(%)

Table 5 Generalization performance metrics of different front-end methods(%)

方法	测试集 Top-1Acc	泛化集 Top-1Acc	泛化集 Macro-F1	泛化集 Macro-Recall	$ \Delta $Top-1Acc
汉明窗	100.0	98.0	87.6	87.1	2.0
db4	100.0	51.2	49.9	51.8	48.8
sym8	100.0	48.8	46.7	50.1	51.2

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表 6 对照条件与固定参数设置

Table 6 Control conditions and fixed parameter settings

参数项	参数
样本数	5000
窗口大小	2560
信号前端处理方法	汉明窗
训练轮次	100
学习率	$1 \times 10^{-4}$
批次大小	32
优化器	Adam
损失函数	交叉熵
Dropout	0.2

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表 7 不同后端模型在噪声条件下性能指标

Table 7 Performance metrics of different back-end models under noisy conditions

模型	Top-1Acc (%)			Macro-F1 (%)			Macro-Recall (%)
SNR (dB)	$ \infty $	10	0	$ \infty $	10	0	$ \infty $	10	0
SVM	23.6	23.6	29.2	9.6	9.6	11.8	25.0	25.0	34.0
RF	100.0	96.8	83.2	100.0	96.8	82.8	100.0	96.6	82.6
MLP	100.0	100.0	92.0	100.0	100.0	91.6	100.0	100.0	91.6
RNN	100.0	100.0	98.2	100.0	100.0	98.0	100.0	100.0	98.2
BiLSTM	100.0	100.0	97.8	100.0	100.0	97.6	100.0	100.0	97.8
TCN	100.0	100.0	96.8	100.0	100.0	96.8	100.0	100.0	96.6
STF	100.0	98.8	90.2	100.0	98.5	91.2	100.0	99.2	90.9
CAM	100.0	100.0	98.8	100.0	100.0	98.8	100.0	100.0	98.5

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表 8 不同后端模型的泛化性能指标

Table 8 Generalization performance metrics of different back-end models

方法	测试集 Top-1Acc	泛化集 Top-1Acc	泛化集 Macro-F1	泛化集 Macro-Recall	$ \Delta $Top-1Acc
SVM	23.6	24.8	9.8	28.8	2.0
RF	100.0	61.4	59.5	51.8	38.6
MLP	100.0	86.6	86.7	90.1	13.4
RNN	100.0	95.0	94.8	94.8	5.0
BiLSTM	100.0	92.4	91.6	91.6	7.6
TCN	100.0	96.8	96.8	96.6	3.2
STF	100.0	89.2	89.2	90.1	10.8
CAM	100.0	98.0	97.6	97.1	2.0

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表 9 不同后端模型的复杂度与计算效率对比

Table 9 Complexity and computational efficiency comparison of different back-end models

模型	参数量(M)	单轮平均训练耗时(s)	单事件推理耗时(ms)
MLP	3.61	1.78	0.14
TCN	0.92	3.64	2.83
RNN	0.46	1.20	0.45
BiLSTM	1.72	7.87	0.71
STF	0.57	2.26	0.56
CAM	0.28	7.98	0.98

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表 10 10 kV动模实验结果

Table 10 Experimental results of the 10 kV dynamic simulation

工况	事件/正确次数	准确率(%)
高阻接地故障	10/10	100
树线放电故障	10/10	100
总结	20/20	100

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表 11 真型实验结果

Table 11 Results of the physical prototype experiment

故障工况	事件/正确次数	准确率(%)
线路1中性点不接地	5/5	100
线路1中性点消弧线圈接地	5/4	80
线路2中性点不接地	5/5	100
线路2中性点消弧线圈接地	5/5	100
总结	20/19	95

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