Transmission line fault classification based on MCCNN-BiLSTM

被引：0
作者：
Shen Y. [1 ]
Xi Y. [1 ]
Chen Z. [1 ]
机构：
[1] School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2022年 / 50卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Bidirectional long and short-term memory neural network; Fault classification; Multi-channel convolutional neural network; Transmission line;
D O I：
10.19783/j.cnki.pspc.210560
中图分类号：
学科分类号：
摘要：
There is a problem that a single-channel fault classifier cannot fully express three-phase fault characteristic information and the classification accuracy is not high. Thus a transmission line fault classification method based on a multi-channel convolutional bidirectional long and short-term memory neural network (MCCNN-BiLSTM) is proposed. This method can input more than one fault three-phase signal at the same time, and can effectively extract the spatial and temporal characteristics of the fault signals, realize the comprehensive extraction of the three-phase fault signal features, and effectively improve the classification accuracy of the neural network. Based on a large amount of fault data analysis of the 735 kV three-phase series compensation transmission line model, no feature extraction algorithm is used for the three-phase fault voltage signal, and only the three-phase voltage amplitude data of the fault period is intercepted as the basic fault characteristic signal input. Simulations show that the network can quickly and accurately classify and identify 11 types of faults, and is not easily affected by factors such as the time of the fault nor excessive resistance. It has good robustness and adaptability. © 2022 Power System Protection and Control Press.
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收藏
页码：114 / 120
页数：6
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