Blind Source Separation of Transformer Acoustic Signal Based on Sparse Component Analysis

Guo Wang (), Yibin Wang, Yongzhi Min and Wu Lei
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Guo Wang: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Yibin Wang: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Yongzhi Min: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Wu Lei: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Energies, 2022, vol. 15, issue 16, 1-15

Abstract: 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: transformer acoustic signal; noise suppression; BSS; SCA; SSP identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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