Application of SCA-VMD in Noise Reduction of Transformer Sound Signal
A method based on sparse component analysis-variational modal decomposition (SCA-VMD) was proposed to separate transformer operation sound signal and reduce noise. The under-determined blind source separation based on sparse characteristics can realize the effective separation of source signals when...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Editorial Department of Power Generation Technology
2020-04-01
|
| Series: | 发电技术 |
| Subjects: | |
| Online Access: | https://www.pgtjournal.com/EN/10.12096/j.2096-4528.pgt.19094 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | A method based on sparse component analysis-variational modal decomposition (SCA-VMD) was proposed to separate transformer operation sound signal and reduce noise. The under-determined blind source separation based on sparse characteristics can realize the effective separation of source signals when the number of observed signals is less than the number of unknown source signals, and the variational modal decomposition (VMD) can decompose a multi-component signal into multiple single-component signals at one time. The two-channel observation signals were used as input, the transformer operation acoustic signals were separated by SCA, the separated signals were decomposed into four-layer intrinsic mode function(IMF) components by VMD, the high frequency components and low frequency components were denoised by the method of threshold filtering, and the denoising signals were reconstructed by the new IMF components. The results of simulation and experiment show that this method can effectively separate and de-noising the operating sound signal of transformer. |
|---|---|
| ISSN: | 2096-4528 |