基于自校验孪生神经网络的故障区段定位方法  

based on self-checking siamese convolutional neural network

作　　者：王毅[1] 李曙 李松浓 陈涛 侯兴哲 付秀元 Wang Yi;Li Shu;Li Songnong;Chen Tao;Hou Xingzhe;Fu Xiuyuan(Communication and Information Engineering College,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Chongqing Electric Power Research Institute,Chongqing 400014,China;State Power Investment Group Digital Technology Co.,Ltd.,Beijing 100080,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065 [2]国网重庆市电力公司电力科学研究院,重庆400014 [3]国家电投集团数字科技有限公司,北京100080

出　　处：《电子技术应用》2022年第7期60-66,73,共8页Application of Electronic Technique

基　　金：重庆市自然科学基金(cstc2016jcyjA0214)。

摘　　要：针对中压配电网区段定位方法所存在的由系统中性点接地方式、故障点距离和过渡电阻大小等环境因素,以及电流互感器极性未知或智能电表错误安装等人为因素所导致的定位不准确问题,提出一种平稳小波极性校验下基于孪生神经网络的故障区段定位方法。首先,分析了零序电流暂态特征,指出了传统线性相关法存在的定位缺陷;其次,使用平稳小波变换解决信号同步和设备反接的问题;最后引入孪生神经网络对故障点上下游信号进行相似性匹配,经训练该模型可以准确定位故障区段。通过仿真验证,该方法具有较强的抗干扰能力,对于定位盲区也有较高的识别率。For medium voltage distribution network segment positioning method,aiming at the inaccurate positioning problem caused by environmental factors such as the system neutral point grounding way,the size of the distance and the transition resistance,as well as human factors such as current transformer polarity unknown or incorrect erection smart meters and so on,this paper puts forward a kind of stationary wavelet polarity check the fault section locating method based on siamese convolutional neural network(S-CNN).Firstly,the transient characteristics of zero-sequence current are analyzed,and the localization defects of traditional linear correlation method are pointed out.Secondly,the stationary wavelet transform(SWT)is used to solve the problems of signal synchronization and equipment reverse connection.Finally,S-CNN is introduced to perform similarity matching for upstream and downstream signals of the fault point,and the model can be trained to locate the fault segment accurately.The simulation results show that this method has strong anti-interference ability and high recognition rate for blind area.

关 键 词：接地故障 故障定位 相似性分析 平稳小波变换 孪生神经网络 

分 类 号：TM773[电气工程—电力系统及自动化]