Open AccessStudy on Denoising Algorithm for Power Quality Disturbances Based on Variational Mode Decomposition
Author(s) -
Yan Li,
Kaicheng Li,
Chang Liu,
Xiangui Xiao,
Xiya Chen,
Menghao Wang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1746/1/012061
Subject(s) - hilbert–huang transform , algorithm , noise reduction , noise (video) , energy (signal processing) , mode (computer interface) , white noise , signal (programming language) , wavelet , power (physics) , decomposition , function (biology) , computer science , mathematics , pattern recognition (psychology) , artificial intelligence , statistics , physics , ecology , quantum mechanics , evolutionary biology , image (mathematics) , biology , programming language , operating system
The power quality signals are always mixed with noise, which will influence the accurate measurement and characteristic extraction. Therefore, it’s important to denoise for the signal processing. This paper puts forward a novel denoising algorithm for power quality disturbances (PQDs) based on variational mode decomposition (VMD) and the truth that the energy density times the average period of each intrinsic mode function (IMF) is a constant. Firstly, the VMD algorithm is used to decompose the white noise into several IMF components. Then, the average period energy product of each IMF component is calculated, and some of the IMF components are selected for reconstruction according to the judgment criterion to obtain the denoised signal. The simulation results show that the proposed algorithm can greatly improve the signal-to-noise ratio (SNR), and the denoising effect is better than that of wavelet threshold and ensemble empirical mode decomposition (EEMD) algorithm.