## 1 Introduction  
As one of the core pieces of equipment, the transformer directly affects the safe, efficient and economic operation of the power system, so it is especially important to ensure the safe and stable operation of the transformer. Partial discharge (PD) is the main cause of transformer insulation damage, and the effect of different types of partial discharge on the transformer insulation system is different, so it is of great practical significance to effectively identify the discharge pattern and take corresponding measures [1, 2]. The traditional PD pattern recognition method mainly includes three parts, which include obtaining the PD data, extracting the PD features and training the pattern recognition classifier [3]. Scholars have done a lot of research on the feature extraction and pattern recognition classifier. The common characteristic parameters include statistical parameters, waveform parameters, fractal characteristic parameters, wavelet parameters, moment characteristic parameters and so on [4, 5, 6, 7, 8, 9]. For pattern recognition methods, back-propagationneural network (BPNN) and support vector machine (SVM) are widely used [10; 11; 12]. However, these machine learning algorithms, which rely on the artificial construction of PD features, are highly subjective and have large recognition errors [13]. With the development of artificial intelligence and computer vision technology, the application of deep neural network to PD pattern recognition can be used to overcome the shortcomings of traditional methods. Although the convolutional neural network (CNN) has the advantage of requiring no artificial computation of signal features, it has not been widely used in the PD pattern recognition. At present, the basic research of CNN includes analyzing the performance differences of convolutional network under different layers, activation functions and pooling modes. The CNN model has a better recognition effect, which improves the accuracy compared with SVM and BPNN [14]. Some literature uses the time domain waveform image of PD as data samples to conduct supervised training on the constructed one-dimensional CNN model [15]. In addition, PRPS spectra obtained from the field experiments and simulation are used as input data, and classical CNN model LeNet-5 is used as classifier [16]. However, these models have fewer layers and cannot fully extract the PD features. The depth model has good recognition accuracy, but it has a large number of parameters and high complexity in terms of model size, which leads to huge network capacity, slow operation speed and high computational resource consumption. Therefore, it is not suitable to embed the deep learning models into devices and it is difficult to deploy them on miniaturized hardware platforms. In order to solve the above problems, a transformer PD pattern recognition method based on MobileNets convolution neural network model is proposed. The model introduces the lightweight attention mechanism Squeeze--and--Excitation (SE) module and h-swish activation function with stronger nonlinear performance based on the use of depth separable convolution module and inverse residual structure. Then, the MCNN network is trained and tested by different types of PD data obtained from laboratory experiments. Analysis results show that the MCNN can significantly reduce the number of parameters and the computational complexity of the model. In addition, MCNN can further improve the recognition accuracy compared with the existing convolutional neural network, which is often used for PD type classification. Finally, the feature maps of the convolution output of the last layer in the MCNN are visually calculated to explain the classification process and verify the function of the model. The remainder of this paper is organized as follows. Data acquisition of PD in transformer is illustrated in Section 2.1; then, the CNN and NCNN (noisy convolution neural network) principle methodology are introduced in Section 2.2.