基于卷积神经网络的绝缘子故障识别算法研究
发布时间：2019-05-30 11:41
【摘要】：卷积神经网络被广泛应用在图像处理领域,不同算法对网络识别率有较大的影响。基于此,引入小波分解理论,通过BP传播算法以及空间向量理论证明得到,相互独立的特征更能表达原图像的信息。通过小波分解去除卷积核之间的相关性,用较少的卷积核提取图像更独立、全面的特征,以提高网络的识别性能。在MNIST、CIFAR-10和CK标准数据库上进行分类识别实验,实验结果表明,此算法能在不同核函数尺寸的条件下取得较高识别率,且达到与传统算法相同识别率的前提下,所需的训练迭代次数更少,训练时间更短。最后,将该算法应用到绝缘子故障识别中,并取得了良好的效果。
[Abstract]:Convolution neural network is widely used in image processing. Different algorithms have great influence on the recognition rate of the network. Based on this, the wavelet decomposition theory is introduced, and it is proved by BP propagation algorithm and spatial vector theory that the independent features can better express the information of the original image. The correlation between convolution kernels is removed by wavelet decomposition, and the image is extracted with fewer convolution kernels to extract more independent and comprehensive features in order to improve the recognition performance of the network. The classification and recognition experiments are carried out on MNIST,CIFAR-10 and CK standard databases. The experimental results show that the algorithm can achieve high recognition rate under the condition of different kernel function sizes, and achieve the same recognition rate as the traditional algorithm. The number of training iterations is less and the training time is shorter. Finally, the algorithm is applied to insulator fault identification, and good results are obtained.
【作者单位】： 华北电力大学电气与电子工程学院;
【分类号】：TM216
[Abstract]:Convolution neural network is widely used in image processing. Different algorithms have great influence on the recognition rate of the network. Based on this, the wavelet decomposition theory is introduced, and it is proved by BP propagation algorithm and spatial vector theory that the independent features can better express the information of the original image. The correlation between convolution kernels is removed by wavelet decomposition, and the image is extracted with fewer convolution kernels to extract more independent and comprehensive features in order to improve the recognition performance of the network. The classification and recognition experiments are carried out on MNIST,CIFAR-10 and CK standard databases. The experimental results show that the algorithm can achieve high recognition rate under the condition of different kernel function sizes, and achieve the same recognition rate as the traditional algorithm. The number of training iterations is less and the training time is shorter. Finally, the algorithm is applied to insulator fault identification, and good results are obtained.
【作者单位】： 华北电力大学电气与电子工程学院;
【分类号】：TM216
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