A Denoising Method for Cable Partial Discharge Signals Based on Image Information Entropy and Multivariate Variational Mode Decomposition

Crosslinked polyethylene (XLPE) insulated cables have been widely developed in transmission lines and urban distribution networks due to their advantages, such as being lightweight, high working temperature, and high transmission power. Partial discharge (PD) detection is the primary means to evaluate the insulation status of XLPE cables. This article proposes a denoising method based on image information entropy (IIE) and a novel adaptive multivariate variational mode decomposition (MVMD) to address the issues of white noise, periodic narrowband interference, and weak adaptability. The method first decomposes the signal based on MVMD, reconstructs and converts it into a grayscale, and then calculates the information entropy. Considering the efficiency of execution, the modal parameters of the algorithm are optimized by combining the correlation coefficient with IIE. Second, the PD feature information is distinguished from the noise interference component by calculating the kurtosis of each intrinsic mode function (IMF) to determine its dominant component's property characteristics and utilizing the kurtosis's sensitivity to noise. Then, the noise interference component is subjected to 3s criteria filtering. Finally, the denoised signal is obtained through a novel, improved wavelet threshold algorithm. The denoising effect of this method is validated by comparing it with several existing methods. The results show that this method has good noise suppression performance for on-site PD signals, with low time consumption, high execution efficiency, and high engineering application value.