基于变分模态分解的光纤电流传感器小波去噪方法

Wavelet de-noising method of all-fiber optical current transformer based on variational mode decomposition

为了提高光纤电流传感器测量信号的信噪比和测量精度,提出一种结合小波分析的变分模态分解去噪方法。该方法利用消除趋势波分析确定变分模态分解层数,并且采用互信息法确定相关模态,筛选出含噪声量较大的不相关模态,对不相关模态用sym8小波变换进行去噪处理,在此基础上进行信号重构,保证信号特征和完整度。在仿真的基础上,对光纤电流传感器的模拟阶跃直流电流和交流谐波电流,分别采用该方法去噪,把方均根误差和信噪比作为衡量去噪效果的指标,并和传统模态分解去噪、经验模态分解去噪和局部均值分解去噪进行比较。仿真结果显示,该方法能有效降低光纤电流传感器输出信号的噪声含量。

In order to improve the signal-to-noise ratio of the all-fiber optical current transformer(FOCT)and improve the measurement accuracy,this paper proposes a variational mode decomposition(VMD)method combined with wavelet.The detrended fluctuation analysis(DFA)is used to determine the number of VMD layers to make the decomposition more accurate,and the mutual information method is used to determine the relevant modes,while the uncorrelated modes with a large amount of noise are processed by sym8 wavelet.Signal reconstruction is carried out on this basis to ensure signal characteristics and integrity.The simulated data FOCT data are processed using this method to testify the efficiency compared with conventional VMD,empirical mode decomposition(EMD)and local mean decomposition(LMD),and the root mean square error(RMSE)and signal to noise ratio(SNR)are used as indicators to measure the denoising effect.It is proved that this method can better reduce the noise of the output signal of FOCT through test results.