基于Hilbert和CA-VMD的风电机组叶轮不平衡故障电信号特征提取方法

Feature Extraction Method for Unbalanced Fault Electrical Signal of Wind Turbine Impeller Based on Hilbert and CA-VMD

针对风电机组叶轮系统故障的非线性、非稳定性和耦合性使早期微弱故障特征频率处于强背景噪声下难以提取的问题,并考虑到传统故障信号采集方法存在的局限性,从电信号入手,提出了一种基于希尔伯特变换和变分模态分解相关性分析(CA-VMD)的风电机组叶轮系统不平衡故障的电信号特征提取方法。首先,针对传统频域分析方法直接对故障电信号进行分析而无法提取故障特征频率的问题,引入Hilbert变换解调出故障调制信号;然后,针对强背景噪声下早期微弱故障特征难提取的问题,引入变分模态分解将故障调制信号分解,并通过相关性分析剔除噪声分量;最后,重构故障调制信号并提取故障特征频率,提高了原始故障信号的信噪比。通过仿真分析,证明了所提方法的有效性。

Aiming at the problem that the non-linearity,instability and coupling of wind turbine impeller system faults make the characteristic frequency of early weak faults difficult to extract under strong background noise, considering the limitations of traditional fault signal acquisition methods,this article starts with electrical signals A method for extracting electrical signal features based on Hilbert transform and variational mode decomposition correlation analysis(CA-VMD)for unbalanced faults in impeller systems of wind turbines is proposed. First,for the problem that the direct analysis of the fault electrical signal in the frequency domain fails to extract the fault characteristic frequency, the Hilbert transform is introduced to demodulate the fault modulation signal. Then,for the problem that the early weak fault feature is difficult to extract under strong background noise,the introduction Variational mode decomposition decomposes the fault modulation signal and removes the noise component through correlation analysis.Finally, the signal is reconstructed and the characteristic frequency of the fault is extracted,which improves the signalto-noise ratio of the fault signal and the sensitivity of early weak fault identification. Through simulation analysis, the effectiveness of the proposed method is verified.