基于卷积神经网络的电网电缆局部放电自动化诊断系统

Automatic Diagnosis System for Partial Discharge of Power Grid Cables Based on Convolutional Neural Network

针对当前电缆局部放电诊断过程中利用信号难以匹配特定放电故障等问题,设计基于卷积神经网络的电网电缆局部放电自动化诊断系统。采集电网电缆局部放电信号,将信号转换为二维图像,将放电信号二维图像作为卷积神经网络的训练样本,训练卷积神经网络;将采集测试样本,基于训练好的卷积神经网络实现电网电缆局部放电自动化诊断。实验结果显示,该系统在卷积核数量为100的条件下,基于GAF时间序列转换图像特征可获取较为满意的诊断结果,降低研究对象局部放电故障时间,提升研究对象运行稳定性。

Design an automated diagnosis system for partial discharge of power grid cables based on convolutional neural networks to address issues such as difficulty in matching specific discharge faults with signals in the current process of cable partial discharge diagnosis.Collect partial discharge signals from power grid cables,convert the signals into two-dimensional images,use the two-dimensional images of discharge signals as training samples for convolutional neural networks,and train the convolutional neural network.Collect test samples and use a trained convolutional neural network to achieve automated diagnosis of partial discharge in power grid cables.The experimental results show that under the condition of 100 convolutional kernels,the system can obtain satisfactory diagnostic results based on GAF time series transformation of image features,reduce the time of partial discharge faults in the research object,and improve the operational stability of the research object.