基于CEEMDAN和卷积神经网络的配电网故障选线新方法OA北大核心CSTPCD
A novel fault line selection method for distribution network based on CEEMDAN and convolutional neural network
文中提出一种基于自适应噪声的完全集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和卷积神经网络(convolutional neural networks,CNN)的配电网故障选线方法.利用CEEMDAN算法分解各线路零序电流信号,得到各线路零序电流的内禀函数;将各线路内禀函数按顺序拼接到一起得到一个时频数据矩阵.时频数据矩阵囊…查看全部>>
A fault line selection method for distribution network based on complete ensemble empirical mode de-composition with adaptive noise(CEEMDAN)and convolutional neural network(CNN)is proposed.Firstly,the CEEMDAN algorithm is used to decompose zero-sequence current signal of each line to obtain the intrinsic function of zero-sequence current of each line.Secondly,the intrinsic functions of each line are spliced together in order to obtain a time-frequency data m…查看全部>>
马红月;李温静;吴文炤;张楠;王婧
国网信息通信产业集团有限公司信通研究院,北京 102200国网信息通信产业集团有限公司信通研究院,北京 102200国网信息通信产业集团有限公司信通研究院,北京 102200国网信息通信产业集团有限公司信通研究院,北京 102200国网信息通信产业集团有限公司信通研究院,北京 102200
动力与电气工程
故障选线CEEMDAN卷积神经网络数据矩阵故障特征向量
fault line selectionCEEMDANconvolutional neural networkdata matrixfault eigenvector
《电测与仪表》 2024 (10)
97-103,7
国家重点研发计划资助项目(2020YFB0905900)
评论