基于二维变分模态分解和Hilbert变换的局放信号特征提取方法

本文通过对二维Hilbert-Huang变换方法的改进，提出了一种基于二维变分模态分解(VMD)和Hilbert变换的局部放电灰度图像特征提取方法。首先，利用局部放电样本生成相应放电灰度图；其次，以二维VMD算法分解各放电灰度图像，获取各个不同中心频率的模态分量；然后，通过四元数Hilbert变换得到各模态函数对应的特征图，并提取灰度纹理特征，构成各放电样本对应的特征向量；最后，以BP神经网络分类器对提取出的局部放电特征量进行分类和识别。实验结果验证表明，同二维Hilbert-Huang变换和传统放电灰度图特征提取方法相比，基于本文方法所得特征量具有更高的正确识别率，验证了该方法的可行性。另外，本文所采用的二维VMD-Hilbert方法为局部放电信号的频谱分析拓展了新的思路。

Feature extraction of partial discharge signals based on 2D variational mode decomposition and Hilbert transform

By improving the 2D Hilbert-Huang transform method, a feature extraction method based on 2D variational mode decomposition (VMD) and Hilbert transform is proposed in this paper. Firstly, the partial discharge (PD) grayscale images are formed by the measured PD samples. Secondly, the PD grayscale images are decomposed by 2D VMD algorithm to obtain the modes on different center frequencies. Then, quaternion Hilbert transform are used to obtain the corresponding characteristic graphs, and the corresponding eigenvector of each PD sample is come into possession by the texture features of these graphs. Finally, the extracted feature quantities are recognized by BP neural network. The experimental results show that compared with 2D Hilbert-Huang transformation and grayscale feature extraction method, the extraction method proposed in this paper has a high correct recognition rate, and it verifies the feasibility of the feature extraction method. In addition, the 2D VMD-Hilbert method also provides a new idea for the spectrum analysis on PD signals.