SCA-VMD在变压器声信号降噪中的应用

针对采集变压器运行声信号时会混入噪声的情况，提出了基于稀疏分量分析-变分模态分解（sparse component analysis-variational modal decomposition，SCA-VMD）分离变压器运行声信号并降噪的方法。基于稀疏特性的欠定盲源分离能够在观测信号数目小于未知源信号数目的情况下实现源信号的有效分离，变分模态分解（VMD）能将一个多分量信号一次性分解为多个单分量信号。以两路观测信号作为输入，利用稀疏分量分析法（SCA）分离得到变压器运行声信号，再利用VMD将分离信号分解为4层本征模态函数（intrinsic mode function，IMF）分量，通过阈值滤波的方法对高频分量和低频分量进行去噪处理，利用新的IMF分量重构得到去噪信号。仿真试验和实际试验结果表明，该方法能实现对变压器运行声信号的有效分离和去噪处理。

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 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.