| 引用本文: | 沈 银,席燕辉,陈子璇.基于多通道卷积双向长短时记忆网络的输电线故障分类[J].电力系统保护与控制,2022,50(3):114-120.[点击复制] |
| SHEN Yin,XI Yanhui,CHEN Zixuan.Transmission line fault classification based on MCCNN-BiLSTM[J].Power System Protection and Control,2022,50(3):114-120[点击复制] |
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| 摘要: |
| 针对单通道故障分类器不能全面表达三相故障特征信息引起分类精度不高的问题，提出了一种基于多通道卷积双向长短时记忆神经网络(MCCNN-BiLSTM)的输电线故障分类方法。该方法可同时输入故障三相信号，并能有效提取故障信号的空间和时间特征，实现了三相故障信号特征的全面提取，有效地提高了神经网络的分类的精度。基于735 kV三相串联补偿输电线模型大量故障数据分析，对三相故障电压信号不采用任何特征提取算法，仅截取故障周期的三相电压幅值数据作为基本故障特征信号输入。仿真实验结果表明：该网络能快速准确地分类识别11种故障，并且不易受故障时刻、过度电阻等因素的影响，具有良好的鲁棒性和适应性。 |
| 关键词: 输电线 多通道卷积神经网络 双向长短时记忆神经网络 故障分类 |
| DOI：DOI: 10.19783/j.cnki.pspc.210560 |
| 投稿时间：2021-05-12修订日期：2021-08-25 |
| 基金项目:国家自然科学基金项目资助(51507015)；湖南省研究生创新项目资助(3040202-012001803) |
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| Transmission line fault classification based on MCCNN-BiLSTM |
| SHEN Yin,XI Yanhui,CHEN Zixuan |
| (School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410004, China) |
| Abstract: |
| 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.
This work is supported by the National Natural Science Foundation of China (No. 51507015). |
| Key words: transmission line multi-channel convolutional neural network bidirectional long and short-term memory neural network fault classification |
