Transmission Line Fault Classification Based on Higher-Order Spectrum and ResNet

Zhenjie Wang, Shanhua Yao
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Abstract

The accurate classification of transmission line faults has been a key issue in the development of smart grids. At present, fault classification is based on recurrent neural network (RNN) for temporal signals, and the development of RNN is not so mature compared with convolutional neural network (CNN). Therefore, this paper proposes a transmission line fault classification algorithm based on higher-order spectral analysis and CNN, aiming at converting the time-series signals into images and using CNN for fault classification. After establishing the fault model on Matlab/Simulink, the current signals of different faults are obtained. After processing the current signals to extract their zero-mode currents, the fault image signals are obtained using higher-order spectral analysis as the input to the CNN. Simulation results show that the proposed method can accurately identify faults with high accuracy when faults occur in transmission lines, thus reducing the economic losses caused by faults.
基于高阶频谱和ResNet的输电线路故障分类
输电线路故障的准确分类一直是智能电网发展的关键问题。目前,故障分类主要是基于递归神经网络(RNN)对时序信号进行分类,与卷积神经网络(CNN)相比,RNN的发展还不够成熟。因此,本文提出了一种基于高阶频谱分析和CNN的传输线故障分类算法,旨在将时间序列信号转换成图像,利用CNN进行故障分类。在Matlab/Simulink上建立故障模型,得到不同故障的电流信号。对电流信号进行处理提取其零模电流后,利用高阶频谱分析得到故障图像信号作为CNN的输入。仿真结果表明,该方法能够在输电线路发生故障时准确、准确地识别故障,从而减少故障造成的经济损失。
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