小样本条件下基于深度特征融合的配电网高阻接地故障识别方法  

High-impedance fault identification method for distribution networks based on deep feature fusion in small sample conditions

作　　者：尚博阳 罗国敏[1] 刘畅宇 王小君[1] 杨雪凤 SHANG Boyang;LUO Guomin;LIU Changyu;WANG Xiaojun;YANG Xuefeng(School of Electrical Engineering,Beijing Jiaotong University,Beijing 100044,China)

机构地区：[1]北京交通大学电气工程学院,北京100044

出　　处：《电力系统保护与控制》2025年第6期101-112,共12页Power System Protection and Control

基　　金：国家自然科学基金项目资助(52377071)。

摘　　要：针对基于数据驱动的高阻接地故障检测可靠性低和样本需求度高的问题,提出一种小样本条件下基于深度特征融合的配电网高阻接地故障识别方法。首先,在配电网高阻故障特性分析的基础上,利用离散小波变换对高阻故障信号的波形进行深度挖掘,构造多尺度时频特征图和全局统计特征矩阵以增强高阻故障特征的表达。其次,结合轻量型残差网络结构和自注意力机制设计深度特征提取网络,实现局部和全局时频特征的融合提取。然后,引入度量元学习计算各类样本在度量空间中的特征类原型以及类原型与样本之间的距离,从而实现高阻故障分类器的构建。最后,在不同复杂运行条件和现场数据集上进行算例测试,验证了所提方法的有效性。To address the issue of low reliability and high sample demand of data-driven high-impedance fault detection,a high-impedance fault identification method for distribution networks based on deep feature fusion in small sample conditions is proposed.First,based on the analysis of high-impedance fault characteristics in distribution networks,the waveform of high-impedance fault signals is deeply mined using discrete wavelet transform,and a multi-scale time-frequency feature graph and global statistical feature matrix are constructed to enhance the expression of high-impedance fault characteristics.Second,combining a lightweight residual network structure and self-attention mechanism,a deep feature extraction network is designed to realize the fusion extraction of local and global time-frequency features.Then,the metric meta-learning is introduced to calculate the feature class prototype and the distance between the class prototype and the sample in the metric space,so as to realize the construction of a high-impedance fault classifier.Finally,the effectiveness of the proposed method is verified by case studies with different complex operating conditions and field data sets.

关 键 词：配电网 高阻接地故障 时频特征融合 小样本 自注意力机 

分 类 号：TM7[电气工程—电力系统及自动化]