LAPSE:2023.25256
Published Article
LAPSE:2023.25256
Blind Source Separation of Transformer Acoustic Signal Based on Sparse Component Analysis
Guo Wang, Yibin Wang, Yongzhi Min, Wu Lei
March 28, 2023
In the acoustics-based power transformer fault diagnosis, a transformer acoustic signal collected by an acoustic sensor is generally mixed with a large number of interference signals. In order to separate transformer acoustic signals from mixed acoustic signals obtained by a small number of sensors, a blind source separation (BSS) method of transformer acoustic signal based on sparse component analysis (SCA) is proposed in this paper. Firstly, the mixed acoustic signals are transformed from time domain to time−frequency (TF) domain, and single source points (SSPs) in the TF plane are extracted by identifying the phase angle differences of the TF points. Then, the mixing matrix is estimated by clustering SSPs with a density clustering algorithm. Finally, the transformer acoustic signal is separated from the mixed acoustic signals based on the compressed sensing theory. The results of the simulation and experiment show that the proposed method can separate the transformer acoustic signal from the mixed acoustic signals in the case of underdetermination. Compared with the existing denoising methods of the transformer acoustic signal, the denoising results of the proposed method have less error and distortion. It will provide important data support for the acoustics-based power transformer fault diagnosis.
Keywords
BSS, noise suppression, SCA, SSP identification, transformer acoustic signal
Suggested Citation
Wang G, Wang Y, Min Y, Lei W. Blind Source Separation of Transformer Acoustic Signal Based on Sparse Component Analysis. (2023). LAPSE:2023.25256
Author Affiliations
Wang G: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; Key Laboratory of Opto-Technology and Intelligent Control Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, China
Wang Y: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Min Y: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; Key Laboratory of Opto-Technology and Intelligent Control Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, China [ORCID]
Lei W: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Journal Name
Energies
Volume
15
Issue
16
First Page
6017
Year
2022
Publication Date
2022-08-19
Published Version
ISSN
1996-1073
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PII: en15166017, Publication Type: Journal Article
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doi:10.3390/en15166017
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