摘要
为有效去除局部放电信号中的噪声干扰,提出改进VMD(Variational Mode Decomposition)算法和L-M神经网络的去噪方法。利用噪声预处理结合分解能量误差自适应地确定VMD算法的最优模态分解层数;引入正态分布直方图区分局部放电信号和窄带干扰信号,重构局部放电信号;利用L-M神经网络对残留白噪声进行拟合滤除。所提方法对仿真和实测信号进行去噪处理,并与传统去噪方法对比。结果表明,所提方法的去噪评估指标更明显,对噪声干扰的去除效果更优。
In order to effectively remove the noise interference in the partial discharge signal,a denoising method based on improved VMD algorithm and L-M neural network is proposed.The optimal mode decomposition levels of VMD algorithm were adaptively determined by noise preprocessing and decomposition energy error.The normal distribution histogram was introduced to distinguish the partial discharge signal from the narrowband interference signal and reconstruct the partial discharge signal.L-M neural network was used to fit and filter the residual white noise.The proposed method was used to denoise the simulated and measured signals,and was compared with the traditional denoising methods.The results show that the denoising evaluation index of the proposed method is more obvious and the removal effect of noise interference is better.
作者
袁莎莎
李梦莹
戴莹莹
江超
杨传凯
薛亮
Yuan Shasha;Li Mengying;Dai Yingying;Jiang Chao;Yang Chuankai;Xue Liang(School of Electronic and Information Engineering,Shanghai University of Electric Power,Shanghai 200090,China;Electric Power Research Institute of State Grid Shaanxi Electric Power Company,Xi’an 710000,Shaanxi,China)
出处
《计算机应用与软件》
北大核心
2025年第2期323-329,373,共8页
Computer Applications and Software
基金
国家自然科学基金项目(62105196)。
关键词
局部放电
VMD算法
L-M神经网络
窄带干扰
白噪声
Partial discharge
VMD algorithm
L-M neural network
Narrowband interference
White noise
