摘要
鉴于局部放电信号受各种噪声的干扰,文章提出一种基于变分模态分解和稀疏分解的局部放电信号去噪算法。以稀疏表示算法为核心,基于局部放电信号的特性构建其过完备字典,再采用匹配追踪算法在过完备字典中搜索出原信号的最佳匹配原子集合重构信号;为解决过完备字典维度过高而导致的搜索次数太多的问题,引进变分模态分解算法和峭度值筛选进行预处理和预重构;优化后的方法可以限制稀疏分解算法的搜索范围和字典参数,以减小计算复杂度。仿真验证以及对工程环境中实测信号的去噪结果表明:该方法具有更好的降噪效果,即使在极低信噪比的情况下,依旧能提取出有效的局部放电信号。
Considering the interference of various noises on partial discharge signals,this paper proposes a partial discharge signal denoising algorithm based on variational modal decomposition and sparse decomposition.Based on the characteristics of partial discharge signals,the sparse representation algorithm is used as the core to construct an overcomplete dictionary,and then the matching and tracking algorithm is used to search for the best matching atomic set of the original signal in the overcomplete dictionary to reconstruct the signal;to solve the problem of excessive search times caused by excessive dimensionality in an overcomplete dictionary,the variational modal decomposition algorithm and kurtosis value screening are introduced for preprocessing and pre reconstruction;the optimized method can limit the search range and dictionary parameters of the sparse decomposition algorithm to reduce computational complexity.Simulation verification and denoising results on measured signals in engineering environments show that this method has better denoising effects,and can still extract effective partial discharge signals even in extremely low signal-to-noise ratios.
作者
钟俊
刘桢羽
赵晓坤
唐妮妮
毕潇文
ZHONG Jun;LIU Zhenyu;ZHAO Xiaokun;TANG Nini;BI Xiaowen(College of Electrical Engineering,Sichuan University,Chengdu 610065,China;State Grid Chengdu Power Supply Company,Chengdu 500642,China)
出处
《现代信息科技》
2024年第1期77-83,共7页
Modern Information Technology
关键词
局部放电信号
变分模态分解
峭度
稀疏表示
机器学习
匹配追踪算法
自适应
partial discharge signal
variational modal decomposition
kurtosis
sparse representation
Machine Learning
matching and tracking algorithm
self-adaption
