中圖分類號(hào)： TM773 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.212354 中文引用格式： 王毅,，李曙,，李松濃，等. 基于自校驗(yàn)孿生神經(jīng)網(wǎng)絡(luò)的故障區(qū)段定位方法[J].電子技術(shù)應(yīng)用,，2022,，48(7)：60-66，73. 英文引用格式： Wang Yi,，Li Shu,，Li Songnong，et al. Fault segment location method based on self-checking siamese convolutional neural network[J]. Application of Electronic Technique,，2022,，48(7)：60-66，73.
Fault segment location method based on self-checking siamese convolutional neural network
Wang Yi1,，Li Shu1,，Li Songnong2，Chen Tao2,，Hou Xingzhe2,，F(xiàn)u Xiuyuan3
1.Communication and Information Engineering College，Chongqing University of Posts and Telecommunications,， Chongqing 400065,，China,； 2.Chongqing Electric Power Research Institute，Chongqing 400014,，China,； 3.State Power Investment Group Digital Technology Co.，Ltd.,，Beijing 100080,，China
Abstract： 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.
