基于迁移学习与残差网络的配电网故障诊断
Fault Diagnosis for Distribution Networks Based on Transfer Learning and Residual Network
- 高压电器 2026年62卷第4期 页码：208-214
- 作者机构：
云南电网有限责任公司红河供电局，云南蒙自 661100
云南电网有限责任公司，昆明 650000
合肥溢鑫电力科技有限公司，合肥 230000
- 作者简介：
周庭栋（1986—），男，工程师，本科，主要从事电网基建工作（Email：1621146145@qq.com）。
尚海（1988—），男，工程师，本科，主要从事主要电网工程建设、工程管理研究（E-mail：715129161@qq.com）。
何永涛（1986—），男，工程师，本科，主要从事输电线路工程工作（E-mail：465238000@qq.com）。
李盼盼（1989—），女，助理工程师，本科，主要从事电力系统自动化研究（通信作者）（E-mail：2150476830@qq.com）。
甘平（1984—），女，助理工程师，本科，主要从事电力系统继电保护研究与应用研究（E-mail：328929758@qq.com）。
- 基金信息：云南电网公司科技项目计划资助。Project Supported by Science and Technology Project of Yunnan Power Grid Company.(YNKJXM20220105)
DOI：10.13296/j.1001-1609.hva.2026.04.024
中图分类号：收稿：2025-09-17，
修回：2025-11-25，
纸质出版：2026-04-16
- 稿件说明：
移动端阅览
周庭栋, 尚海, 何永涛, 等. 基于迁移学习与残差网络的配电网故障诊断[J]. 高压电器, 2026,62(4):208-214.
ZHOU Tingdong, SHANG Hai, HE Yongtao, et al. Fault Diagnosis for Distribution Networks Based on Transfer Learning and Residual Network[J]. High Voltage Apparatus, 2026, 62(4): 208-214.
周庭栋, 尚海, 何永涛, 等. 基于迁移学习与残差网络的配电网故障诊断[J]. 高压电器, 2026,62(4):208-214. DOI： 10.13296/j.1001-1609.hva.2026.04.024.
ZHOU Tingdong, SHANG Hai, HE Yongtao, et al. Fault Diagnosis for Distribution Networks Based on Transfer Learning and Residual Network[J]. High Voltage Apparatus, 2026, 62(4): 208-214. DOI： 10.13296/j.1001-1609.hva.2026.04.024.
摘要
配电网单相接地故障占比较高，存在多种故障类型。如果对不同类型的接地故障进行适当的处理，能有效提高配电网的可靠性和可用性。然而，如何快速区分不同的故障类型，一直以来是一个研究热点。本研究针对高阻接地故障电流和电压特征不明显导致的高阻接地故障检测困难的问题，提出了一种创新的解决方法，即采用迁移学习和深度残差网络对高阻接地故障进行识别。首先，提取单相接地故障后2个周期内的零序电压数据，利用小波变换将零序电压映射为二维时频图；其次，搭建深度残差网络，通过冻结底层参数实现小样本迁移，微调参数对不同的故障样本分类。为了验证算法有效性，搭建了10 kV配电网模型进行仿真。经实验证明，采用该方法可有效检测弧光高阻接地故障信号，并准确识别出这类接地故障。
Abstract
The single-phase grounding faults in the distribution network account for a relatively high proportion
and there are various types of faults. If different types of grounding faults are handled appropriately
both reliability and availability of the distribution network can be effectively improved. However
how to quickly distinguish different types of faults has always been a research hotspot. In this study
an innovative solution is proposed to solve the problem of high resistance grounding fault detection difficulties caused by the unobvious characteristics of high resistance grounding fault current and voltage
that is
the high resistance grounding fault is identified by using transfer learning and deep residual network. First
the zero-sequence voltage data in 2 cycles after the single-phase ground fault is extracted
and the zero-sequence voltage is mapped into the two-dimensional time-frequency map. Then
a deep residual network is built
the small sample migration is achieved by freezing underlying parameters
and different fault samples is classified by fine-tuning parameters.To verify the effectiveness of the algorithm
a 10 kV distribution network simulation model is set up. It is proved by experiment that application of this method can effectively detect arc high resistance grounding fault signals and accurately identify such grounding faults.
关键词
Keywords
references
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