基于EMD-PSD的OLTC振动信号特征提取方法

Vibration signal feature extraction method of the on-load tap changer based on EMD-PSD

在有载分接开关(OLTC)故障案例中机械故障占很大的比例,为确保OLTC可靠运行,提出一种基于EMD-PSD的OLTC振动信号特征提取方法。首先,模拟OLTC机械故障,采集振动信号,并根据奇异熵确定奇异值分解(SVD)的降噪阶次进行小波包-SVD降噪;其次,对降噪后的信号进行经验模态分解(EMD),并求其功率谱密度(PSD);最后,运用功率谱密度能量构造特征量。实验结果表明,基于奇异熵选择SVD降噪阶次,可以提高振动信号的信噪比,并且将EMD和PSD算法结合可以有效地提取OLTC故障特征。

Most reasons causing the on-load tap changer(OLTC)un-functional are mechanical faults.In order to ensure the reliable operation of the OLTC,a kind of vibration feature extraction method of OLTC based on the empirical mode decomposition-power spectral density(EMD-PSD)algorithm is proposed.Firstly,several mechanical faults of the OLTC is simulated and the vibration signal is collected.The noise of vibration signal is eliminated by the wavelet packet firstly.And then a reasonable order for noise reduction is selected according to the singular entropy which is to eliminate the noise of vibration signal by singular value decomposition(SVD).Secondly,the noise-reduced signals are decomposed by EMD and the power spectral density of the IMF is obtained.Finally,the PSD energy is used to construct the eigenvectors.It is shown that the signal-to-noise ratio of the vibration signal can be improved by selecting the SVD order of noise reduction based on the singular entropy.Based on the combination of EMD and PSD methods,the fault features of the OLTC can be effectively extracted.