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
计量
- 文章访问数: 193
- HTML全文浏览量: 334
- 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 interests include power distribution network fault identification

CAO Yi-Jia　Professor at Changsha University of Science & Technology. His research interests include power system security and control and intelligent optimal dispatching of large-scale power grids

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

ZHONG Jun-Jie　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; 构建分尺度跨线路处理模型, 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

HTML全文

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

下载: 全尺寸图片幻灯片

图 2 高阻接地故障辨识流程图

Fig. 2 Flowchart of high-impedance grounding fault identification

下载: 全尺寸图片幻灯片

图 3 10 kV配电网仿真模型

Fig. 3 10 kV power distribution network simulation model

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

图 5 各方法对比度与泄漏比柱状图

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

图 8 不同模型训练损失性能对比

Fig. 8 Training loss performance comparison of various models

下载: 全尺寸图片幻灯片

图 9 不同后端方法在噪声条件下的性能下降对比

Fig. 9 Performance degradation comparison of different back-end methods under noise conditions

下载: 全尺寸图片幻灯片

图 10 消融实验结果对比

Fig. 10 Comparison of ablation experiment results

下载: 全尺寸图片幻灯片

图 11 10 kV动模实验系统拓扑

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

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

图 13 高阻接地实验现场

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

下载: 全尺寸图片幻灯片

图 14 树线放电实验现场

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

下载: 全尺寸图片幻灯片

图 15 真型实验系统拓扑

Fig. 15 Topology of the physical prototype experimental system

下载: 全尺寸图片幻灯片

图 16 真型实验系统现场图

Fig. 16 Field photograph of the physical prototype experimental system

下载: 全尺寸图片幻灯片

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

下载: 导出CSV

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	$\{cA_4,\; cD_4,\; cD_3\}$

下载: 导出CSV

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

下载: 导出CSV

表 4 不同前端方法在噪声条件下的性能指标

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

下载: 导出CSV

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

下载: 导出CSV

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

下载: 导出CSV

表 7 不同后端模型在噪声条件下的性能指标

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

下载: 导出CSV

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	−1.2
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

下载: 导出CSV

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

下载: 导出CSV

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

下载: 导出CSV

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

下载: 导出CSV

参考文献(32)

[1]	王成山, 李鹏. 分布式发电、微网与智能配电网的发展与挑战. 电力系统自动化, 2010, 34(2): 10−14 Wang Cheng-Shan, Li Peng. Development and challenges of distributed generation, the micro-grid and smart distribution system. Automation of Electric Power Systems, 2010, 34(2): 10−14
[2]	缪希仁, 赵丹, 刘晓明, 洪翠, 庄胜斌. 含分布式电源配电网短路保护研究综述. 高电压技术, 2023, 49(7): 3006−3019 doi: 10.13336/j.1003-6520.hve.20220922 Miao Xi-Ren, Zhao Dan, Liu Xiao-Ming, Hong Cui, Zhuang Sheng-Bin. A research review of short-circuit protection in distribution network with distributed generation. High Voltage Engineering, 2023, 49(7): 3006−3019 doi: 10.13336/j.1003-6520.hve.20220922
[3]	Russell B D, Benner C L. Arcing fault detection for distribution feeders: Security assessment in long term field trials. IEEE Transactions on Power Delivery, 1995, 10(2): 676−683 doi: 10.1109/61.400864
[4]	耿建昭, 王宾, 董新洲, Dominik B. 中性点有效接地配电网高阻接地故障特征分析及检测. 电力系统自动化, 2013, 37(16): 85−91 doi: 10.7500/AEPS201211080 Geng Jian-Zhao, Wang Bin, Dong Xin-Zhou, Dominik B. Analysis and detection of high impedance grounding fault in neutral point effectively grounding distribution network. Automation of Electric Power Systems, 2013, 37(16): 85−91 doi: 10.7500/AEPS201211080
[5]	王宾, 耿建昭, 董新洲. 配网高阻接地故障伏安特性分析及检测. 中国电机工程学报, 2014, 34(22): 3815−3823 doi: 10.13334/j.0258-8013.pcsee.2014.22.029 Wang Bin, Geng Jian-Zhao, Dong Xin-Zhou. Analysis and detection of volt-ampere characteristics for high impedance faults in distribution systems. Proceedings of the CSEE, 2014, 34(22): 3815−3823 doi: 10.13334/j.0258-8013.pcsee.2014.22.029
[6]	王伟杰, 曾鑫洁, 徐远途, 李书仪, 阮灿华, 童宁, 等. 基于正序突变量相轨迹辨识的可再生能源配电网电流保护. 发电技术, 2024, 45(4): 753−764 doi: 10.12096/j.2096-4528.pgt.23078 Wang Wei-Jie, Zeng Xin-Jie, Xu Yuan-Tu, Li Shu-Yi, Ruan Can-Hua, Tong Ning, et al. Renewable energy distribution network overcurrent protection based on positive-sequence sudden-change component locus identification. Power Generation Technology, 2024, 45(4): 753−764 doi: 10.12096/j.2096-4528.pgt.23078
[7]	郭谋发, 游建章, 郑泽胤. 配电网单相接地故障柔性消弧技术综述. 高电压技术, 2023, 49(10): 4232−4246 doi: 10.13336/j.1003-6520.hve.20221889 Guo Mou-Fa, You Jian-Zhang, Zheng Ze-Yin. Review of flexible arc suppression technology for single-phase-to-ground fault in distribution networks. High Voltage Engineering, 2023, 49(10): 4232−4246 doi: 10.13336/j.1003-6520.hve.20221889
[8]	朱晓娟, 林圣, 张姝, 何正友. 基于小波能量矩的高阻接地故障检测方法. 电力自动化设备, 2016, 36(12): 161−168 doi: 10.16081/j.issn.1006-6047.2016.12.025 Zhu Xiao-Juan, Lin Sheng, Zhang Shu, He Zheng-You. High-impedance grounding fault detection based on wavelet energy moment. Electric Power Automation Equipment, 2016, 36(12): 161−168 doi: 10.16081/j.issn.1006-6047.2016.12.025
[9]	林万里, 杨耿杰, 郭谋发. 基于零休偏移现象的弧光高阻接地故障类型辨识. 电网技术, 2026, 50(4): 1790−1799 doi: 10.13335/j.1000-3673.pst.2025.0173 Lin Wan-Li, Yang Geng-Jie, Guo Mou-Fa. Arc high-impedance fault identification based on zero-crossing offset phenomenon. Power System Technology, 2026, 50(4): 1790−1799 doi: 10.13335/j.1000-3673.pst.2025.0173
[10]	袁智勇, 白浩, 邵向潮, 潘姝慧, 雷金勇, 王康, 等. 基于一维多尺度轻量级DenseNet的配电网高阻接地故障检测方法. 南方电网技术, 2022, 16(6): 33−43 doi: 10.13648/j.cnki.issn1674-0629.2022.06.004 Yuan Zhi-Yong, Bai Hao, Shao Xiang-Chao, Pan Shu-Hui, Lei Jin-Yong, Wang Kang, et al. Detection method of high impedance grounding fault in distribution network based on one-dimensional multi-scale lightweight DenseNet. Southern Power System Technology, 2022, 16(6): 33−43 doi: 10.13648/j.cnki.issn1674-0629.2022.06.004
[11]	刘科研, 詹惠瑜, 谭云耀, 柯亮, 李波, 廖凯. 基于改进极限学习机的配电网高阻接地故障检测方案. 电网技术, 2023, 47(5): 1886−1896 doi: 10.13335/j.1000-3673.pst.2022.1908 Liu Ke-Yan, Zhan Hui-Yu, Tan Yun-Yao, Ke Liang, Li Bo, Liao Kai. High impedance grounding fault detection in distribution network based on improved extreme learning machine. Power System Technology, 2023, 47(5): 1886−1896 doi: 10.13335/j.1000-3673.pst.2022.1908
[12]	Silva S, Costa P, Gouvea M, Lacerda A, Alves F, Leite D. High impedance fault detection in power distribution systems using wavelet transform and evolving neural network. Electric Power Systems Research, 2018, 154: 474−483 doi: 10.1016/j.epsr.2017.08.039
[13]	柯亮, 李波, 廖凯, 谭云耀, 贾亦真, 段青, 等. 基于XGBoost的配电网高阻接地故障检测方法. 电力系统保护与控制, 2024, 52(6): 88−98 doi: 10.19783/j.cnki.pspc.231322 Ke Liang, Li Bo, Liao Kai, Tan Yun-Yao, Jia Yi-Zhen, Duan Qing, et al. High impedance fault detection method in a distribution network based on XGBoost. Power System Protection and Control, 2024, 52(6): 88−98 doi: 10.19783/j.cnki.pspc.231322
[14]	韩笑, 郭剑波, 蒲天骄, 傅凯, 乔骥, 王新迎. 电力人工智能技术理论基础与发展展望(一): 假设分析与应用范式. 中国电机工程学报, 2023, 43(8): 2877−2890 doi: 10.13334/j.0258-8013.pcsee.213030 Han Xiao, Guo Jian-Bo, Pu Tian-Jiao, Fu Kai, Qiao Ji, Wang Xin-Ying. Theoretical foundation and directions of electric power artificial intelligence (I): Hypothesis analysis and application paradigm. Proceedings of the CSEE, 2023, 43(8): 2877−2890 doi: 10.13334/j.0258-8013.pcsee.213030
[15]	蒲天骄, 乔骥, 赵紫璇, 赵鹏. 面向电力系统智能分析的机器学习可解释性方法研究(一): 基本概念与框架. 中国电机工程学报, 2023, 43(18): 7010−7029 doi: 10.13334/j.0258-8013.pcsee.221366 Pu Tian-Jiao, Qiao Ji, Zhao Zi-Xuan, Zhao Peng. Research on interpretable methods of machine learning applied in intelligent analysis of power system (Part I): Basic concept and framework. Proceedings of the CSEE, 2023, 43(18): 7010−7029 doi: 10.13334/j.0258-8013.pcsee.221366
[16]	张浩, 张大海, 刘乃毓, 吴奎忠, 侍哲. 基于改进VMD及ConvNeXt的小电流接地系统单相接地故障选线方法. 高电压技术, 2025, 51(2): 730−741 doi: 10.13336/j.1003-6520.hve.20240348 Zhang Hao, Zhang Da-Hai, Liu Nai-Yu, Wu Kui-Zhong, Shi Zhe. Line selection method of single-phase grounding faults for low-current grounding system based on improved VMD and ConvNeXt. High Voltage Engineering, 2025, 51(2): 730−741 doi: 10.13336/j.1003-6520.hve.20240348
[17]	汤涛, 黄纯, 江亚群, 罗勋华, 谢兴, 刘鹏辉. 基于高低频段暂态信号相关分析的谐振接地故障选线方法. 电力系统自动化, 2016, 40(16): 105−111 doi: 10.7500/AEPS20151125013 Tang Tao, Huang Chun, Jiang Ya-Qun, Luo Xun-Hua, Xie Xing, Liu Peng-Hui. Fault line selection method in resonant earthed system based on transient signal correlation analysis under high and low frequencies. Automation of Electric Power Systems, 2016, 40(16): 105−111 doi: 10.7500/AEPS20151125013
[18]	Cao G, Luo S L, Zhu J H. Research on automatic line selection method for single-phase ground fault in active distribution network. In: Proceedings of the 4th Conference on Energy Internet and Energy System Integration (EI2). Wuhan, China: IEEE, 2020. 2330−2334
[19]	孔祥轩, 郑楚韬, 仇志成, 王伟冠, 黄焯麟, 何其淼, 等. 基于广义深度学习的含DG配网故障诊断方法. 电网与清洁能源, 2020, 36(11): 53−57 doi: 10.3969/j.issn.1674-3814.2020.11.007 Kong Xiang-Xuan, Zheng Chu-Tao, Qiu Zhi-Cheng, Wang Wei-Guan, Huang Zhuo-Lin, He Qi-Miao, et al. Fault location of distribution network with DG based on GDL. Power System and Clean Energy, 2020, 36(11): 53−57 doi: 10.3969/j.issn.1674-3814.2020.11.007
[20]	崔朴奕, 李国丽, 张倩, 范明豪, 张运勇. 基于VMD-CNN的小电流接地系统故障电弧检测方法研究. 电力系统保护与控制, 2021, 49(23): 18−25 doi: 10.19783/j.cnki.pspc.211222 Cui Pu-Yi, Li Guo-Li, Zhang Qian, Fan Ming-Hao, Zhang Yun-Yong. A fault arc detection method of a small current grounding system based on VMD-CNN. Power System Protection and Control, 2021, 49(23): 18−25 doi: 10.19783/j.cnki.pspc.211222
[21]	Li H, Chen J B, Qiu J, Du X, Dai M, Chen H R. Research on fault line selection for single phase grounding in active distribution network based on softmax regression. In: Proceedings of the China International Conference on Electricity Distribution (CICED). Tianjin, China: IEEE, 2018. 1246−1250
[22]	李政洋, 曹一家, 陈春, 李妍莎, 李景禄, 赵龙. 含直配式新能源的配电网接地故障协同处理与区域辨识. 中国电机工程学报, 2025, 45(6): 2145−2159 doi: 10.13334/j.0258-8013.pcsee.231889 Li Zheng-Yang, Cao Yi-Jia, Chen Chun, Li Yan-Sha, Li Jing-Lu, Zhao Long. Collaborative fault handling and area identification of grounding faults in distribution networks with directly-coupled renewable energy sources. Proceedings of the CSEE, 2025, 45(6): 2145−2159 doi: 10.13334/j.0258-8013.pcsee.231889
[23]	韩笑, 罗维真, 王春蘅. 基于同步挤压小波变换的配电网单相高阻接地故障选线. 科学技术与工程, 2019, 19(15): 150−156 doi: 10.3969/j.issn.1671-1815.2019.15.024 Han Xiao, Luo Wei-Zhen, Wang Chun-Heng. High-impedance grounding fault selection in the distribution network using synchrosqueezed wavelet transforms. Science Technology and Engineering, 2019, 19(15): 150−156 doi: 10.3969/j.issn.1671-1815.2019.15.024
[24]	李子豪, 李雪莹, 李永庆, 颜军, 孙媛媛. WTANet: 基于多尺度小波变换注意力的高分遥感影像变化检测方法. 地球信息科学学报, 2025, 27(8): 1920−1935 doi: 10.12082/dqxxkx.2025.250186 Li Zi-Hao, Li Xue-Ying, Li Yong-Qing, Yan Jun, Sun Yuan-Yuan. WTANet: Multi-scale wavelet transform attention network for change detection in high-resolution remote sensing. Journal of Geo-Information Science, 2025, 27(8): 1920−1935 doi: 10.12082/dqxxkx.2025.250186
[25]	刘科研, 叶学顺, 李昭, 谭云耀, 李波. 基于多分辨率小波变换的配电网高阻接地故障检测方法. 高电压技术, 2023, 49(10): 4247−4256 doi: 10.13336/j.1003-6520.hve.20220027 Liu Ke-Yan, Ye Xue-Shun, Li Zhao, Tan Yun-Yao, Li Bo. Detection method of high impedance fault in distribution network based on multi-resolution wavelet transform. High Voltage Engineering, 2023, 49(10): 4247−4256 doi: 10.13336/j.1003-6520.hve.20220027
[26]	袁涛, 杨庆, 周桐, 刘红文, 李瑞桂. 基于零序电流非线性畸变特性的弧光高阻接地初期故障辨识方法. 中国电机工程学报, 2025, 45(8): 3248−3259 doi: 10.13334/j.0258-8013.pcsee.232806 Yuan Tao, Yang Qing, Zhou Tong, Liu Hong-Wen, Li Rui-Gui. Detection method of arc high impedance grounding initial fault based on nonlinear distortion characteristics of zero-sequence current. Proceedings of the CSEE, 2025, 45(8): 3248−3259 doi: 10.13334/j.0258-8013.pcsee.232806
[27]	罗小同, 杨汶锦, 曲延云, 谢源. 基于全局局部协同的非均匀图像去雾方法. 自动化学报, 2024, 50(7): 1333−1344 doi: 10.16383/j.aas.c230567 Luo Xiao-Tong, Yang Wen-Jin, Qu Yan-Yun, Xie Yuan. Dehazeformer: Nonhomogeneous image dehazing with collaborative global-local network. Acta Automatica Sinica, 2024, 50(7): 1333−1344 doi: 10.16383/j.aas.c230567
[28]	陈俊英, 席月芸, 李朝阳. 多尺度局部特征和Transformer全局学习融合的发动机剩余寿命预测. 自动化学报, 2024, 50(9): 1818−1830 doi: 10.16383/j.aas.c230634 Chen Jun-Ying, Xi Yue-Yun, Li Zhao-Yang. Prediction of aeroengine remaining life by combining multi-scale local features and Transformer global learning. Acta Automatica Sinica, 2024, 50(9): 1818−1830 doi: 10.16383/j.aas.c230634
[29]	Harris F J. On the use of windows for harmonic analysis with the discrete Fourier transform. Proceedings of the IEEE, 1978, 66(1): 51−83 doi: 10.1109/PROC.1978.10837
[30]	张恒, 苏飞. 基于全相位滤波的卷积窗设计. 信号处理, 2019, 35(10): 1732−1738 doi: 10.16798/j.issn.1003-0530.2019.10.014 Zhang Heng, Su Fei. Design of convolution window based on all-phase filtering. Journal of Signal Processing, 2019, 35(10): 1732−1738 doi: 10.16798/j.issn.1003-0530.2019.10.014
[31]	高云鹏, 李峰, 陈婧, 曹一家. 基于Rife-Vincent窗频谱校正的介损因数测量. 湖南大学学报(自然科学版), 2014, 41(2): 85−90 Gao Yun-Peng, Li Feng, Chen Jing, Cao Yi-Jia. Dielectric loss factor measurement based on Rife-Vincent window spectral correction. Journal of Hunan University (Natural Sciences), 2014, 41(2): 85−90
[32]	Avci K. Fractional Kaiser window with application to finite impulse response digital filter design. IEEE Access, 2024, 12: 155549−155563 doi: 10.1109/ACCESS.2024.3484930