Partial Discharge Signal Denoising Algorithm Based on Variational Modal Decomposition and Sparse Representation
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.
partial discharge signalvariational modal decompositionkurtosissparse representationMachine Learningmatching and tracking algorithmself-adaption
国家科技期刊平台
NSTL
万方数据
维普
