摘要
针对电力设备局部放电图谱的识别问题,提出一种改进交叉熵损失函数的双路径全卷积神经网络模型。使用局放图谱作为模型输入,采用双路径的方式,两路使用不同大小卷积核分别提取图谱较深层和较浅层特征,然后进行特征融合。使用卷积层代替全连接层,更多保留局放特征间的空间关联性。改进的交叉熵损失函数可以使模型更适用于数据集样本不均衡的情况。实验结果表明,改进FCN双路径特征融合识别方法准确率达到99.31%,可以准确识别局放图谱,且模型参数量更小。
A fully convolutional dual-path neural network model with improved cross-entropy loss function is proposed to solve the problem of identifying partial discharge maps of electrical equipment.Using the partial discharge map as the model input,the deep and shallow features of the map are extracted by two channels using different size convolution kernels,and then performing feature fusion.The convolutional layer is used instead of the fully connection layer to preserve more spatial correlation between PD features.The improved cross-entropy loss function can make the model more suitable for the situation of imbalanced datasets.The experimental results show that the accuracy of the improved FCN dual-path feature fusion recognition method reaches 99.31%,which can accurately identify the partial discharge map,and the amount of model parameters is smaller.
作者
金玉
袁和金
Jin Yu;Yuan Hejin(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China;Engineering Research Center of Intelligent Computing for Complex Energy Systems,Ministry of Education,Baoding 071003,China)
出处
《电子测量技术》
北大核心
2022年第24期132-136,共5页
Electronic Measurement Technology
关键词
局部放电
卷积神经网络
特征融合
交叉熵损失函数
partial discharge
convolutional neural network
feature fusion
cross entropy loss function
作者简介
通信作者:金玉,硕士研究生,主要研究方向为图像处理等。E-mail:243923047@qq.com;袁和金,博士,副教授,硕士生导师,主要研究方向为模式识别和计算机视觉、图形与图像处理。E-mail:yhj_1977@163.com