基于频域特征与改进KAN网络的输电线路故障识别方法  

Transmission line fault identification method based on frequency feature and improved KAN network

作　　者：张照辉 李洪涛 徐阳 赵科 ZHANG Zhaohui;LI Hongtao;XU Yang;ZHAO Ke(Electric Power Research Institute of State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 211103,China)

机构地区：[1]国网江苏省电力有限公司电力科学研究院,江苏南京211103

出　　处：《测绘通报》2025年第1期6-11,共6页Bulletin of Surveying and Mapping

基　　金：国网江苏省电力有限公司科技项目(J2020108)。

摘　　要：输电线路故障识别准确率对提高电力系统的供电可靠性至关重要,针对已有方法难以有效识别出线路对树木放电、风偏、雷击、鸟害、山火、外力破坏和异物等复杂外部原因的问题,本文提出了一种基于频域特征与改进KAN网络的输电线路故障识别方法。首先,分析了不同故障下如外力破坏、山火、异物等的三相数据,引入了二维分数阶频域变换方法提取出故障深度特征;然后,提出了一种融入自注意力和卷积模块的柯尔莫格罗夫-阿诺德网络(SCKAN),并改进了小波基函数用于网络权重初始化;最后,通过采集到的输电线路真实数据,验证了所提方法的有效性。结果表明,本文方法极大地增强了输电线路故障的识别能力。The fault identification accuracy in transmission lines is crucial for improving the reliability of power supply in the power system.To solve the problem that existing methods are difficult to effectively identify complex external factors such as tree discharge,wind deviation,lightning strikes,bird damage,wildfires,external damage,and foreign objects caused by power lines,a fault identification method for transmission lines based on frequency features and an improved KAN network is proposed.Firstly,the three-phase data under different faults such as external force damage,wildfire,foreign objects,etc.are analyzed,and a fractional frequency transformation method is introduced to extract the depth features of the faults.Then,a self-attention convolutional Kolmogorov-Arnold network(SCKAN)incorporating self-attention and convolution module is proposed,and the improved wavelet basis function is used for network weight initialization.Finally,the proposed methods effectiveness is verified through experiments based on the collected real data of transmission lines.The results show that the proposed method greatly enhances the ability to identify transmission line faults.

关 键 词：频域特征 注意力机制 柯尔莫格罗夫-阿诺德网络 输电线路故障识别 

分 类 号：P237[天文地球—摄影测量与遥感]