###### Abstract
[PERSON] [26] proposed an empirical mode decomposition (EMD)-based method for partial discharge signal noise reduction. The noisy signal is decomposed into modal components of different frequencies, the relationship between the modal components is determined according to the cross-correlation method, and the effective modal components are selected for reconstruction so as to achieve signal noise reduction. But components with similar frequencies can be separated in different modes, thereby affecting the accuracy of noise reduction. [PERSON] [64] introduced an automatic pattern recognition technique based on partial discharge phase spectrum K-means clustering, and the technique is applied to recognize partial discharge pattern recognition without phase reference information and to suppress interference signals. But this technology may be sensitive to initial conditions and the number of clusters. [PERSON] [65] computed representative parameters linked to the shape of every pulse waveform by utilizing signal feature generation algorithms, allowing them to be separated into different clusters. This effectively classifies the measured partial discharge signals and pulsed noise interference in power cables. [PERSON] [66] employed the high frequency separation technique to discriminate four kinds of partial discharge, extracting characteristic parameters. By selecting appropriate kernel functions, optimizing support vector machines (SVMs) with principal component analysis (PCA) was performed. The recognition precision can reach 97%, but it requires extensive optimization and feature extraction processes. [PERSON] [67] proposed a method for identifying different sources of partial discharge by utilizing the combined principal of PCA and SVMs. Three features most representative of the original data were selected from ten features. A support vector machine recognition model was developed by utilizing the grey wolf optimization (GWO) algorithm to optimize the kernel factor g and the non-negative punishment function c. This approach involved tailoring the SVM model through GWO techniques to enhance its performance. But, its optimization process is time-consuming, resulting in limited usage scenarios. [PERSON]. [68] conducted experimental research on the partial discharge phenomenon of internal insulation in transformers. They used wavelet packet analysis technology to perform time-frequency decomposition and feature extraction on the signal, thereby obtaining fractal dimensions on different frequency bands. The reciprocal of fractal dimensions was used as the feature parameter and input into an improved support vector machine. The recognition results indicate that even with a small number of samples, this method can still achieve a high recognition rate and fast convergence speed. However, this method may not be able to capture all necessary signal features in complex signals due to its dependence on fractal dimensions. [PERSON] et al. [69] proposed a partial discharge pattern recognition method based on deep belief network (DBN); collected three discharge signals of corona discharge, internal discharge, and surface discharge; constructed vector parameterization (PRPD) of phase decomposition partial discharge; and extracted four different features. Three different discharge signals can be classified by using DBN. [PERSON] [70] carried out laboratory tests on different types of artificial manufacturing defects in ethylene propylene rubber (EPR) cables and carried out partial discharge tests. Features were derived from the mixed signals and a convolutional neural network model was used for signal classification and recognition, demonstrating higher accuracy than SVMs and back-propagation neural networks. But, it is time-consuming due to manual feature extraction. In comparison to the methods mentioned above, this paper proposes a partial discharge classification model based on wavelet transform and DBN (WT-DBN) for accurate identification of corona interference in partial discharge signals of vehicle-mounted cable termination.