# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 1 Introduction  
Electrical equipment is a critical element of power systems, and its safety and stability have a significant effect on the overall reliability of system operations [1; 2; 3]. Proper identification of partial discharge (PD) and its various types is fundamental for evaluating the operational health of electrical equipment [4].  
For effective pattern recognition, it is crucial to have a sufficient quantity of training samples with a well-balanced category distribution [5; 6]. However, the available PD samples for electrical equipment are limited, and the category distribution is uneven [7; 8]. The small sample size reduces the available information about the PD sample probability distribution, making it difficult to develop a reliable pattern recognition model. This imbalance causes the minority class samples to receive inadequate attention, resulting in poor recognition accuracy for these samples.  
The current mainstream method for solving the problem of sample imbalance is to perform data augmentation on the sample [9]. In data augmentation methods, undersampling [10], random over-sampling [11] (ROS), and the synthetic minority oversampling technique [12] (SMOTE) can lead to missing or biased probability distributions of the generated imbalanced samples, resulting in overfitting of the classifier. Sample generation methods based on deep learning have emerged in imbalanced sample oversampling methods in recent years [13; 14]. [PERSON] et al. [15] utilized a Variational Autoencoder (VAE) for PD data matching. VAE-generated samples tend to be smooth but face challenges suchas inter-class blurriness and mode collapse, which limits the diversity of the generated data [16]. The Boundary Equilibrium Generative Adversarial Network (BEGAN) [17], utilizing an autoencoder as its discriminator along with a balancing strategy, allows for quick and stable convergence. While BEGAN generates PD samples that closely resemble the originals, issues like mode collapse and limited diversity persist. [PERSON] et al. [18] applied the Conditional Wasserstein GAN with gradient penalty (CWGAN-GP) to augment transformer fault samples, using the Wasserstein distance as the objective function with a gradient penalty term. This approach effectively mitigates gradient vanishing and mode collapse and supports multi-class sample generation. However, CWGAN-GP struggles with slow convergence when generating high-dimensional samples, often leading to either overly sharp or noisy outputs. In comparison, models like VAE and BEGAN tend to produce more realistic samples. The currently proposed generative models aim solely at approximating the distribution of samples within the same class without utilizing the probabilistic information in the sample supplement set, indicating room for improvement in the accuracy of fitting the target distribution.  
To enhance PD pattern recognition accuracy in scenarios with imbalanced and small sample sizes, this paper proposes a data augmentation and recognition method based on a Generative Adversarial Network integrated with a Deep Autoencoder (DAE-GAN) [19]. The approach uses denoised PD pulses as input and embeds a deep autoencoder into the sample generation process of CWGAN-GP. The DAE improves the realism of generated samples while addressing sample imbalance by creating additional PD samples. During training, complementary samples are introduced to utilize probabilistic information in the dataset, aiding in better probability distribution fitting and mitigating the small sample issue. The generated data is then used to fine-tune the discriminator, which serves as a classifier for PD pattern recognition. Experimental results indicate that the proposed method, under imbalanced and small sample conditions, significantly enhances sample authenticity, improves probability distribution fitting, and boosts PD pattern recognition accuracy.