# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 5 Conclusions  
To address the issue of low accuracy in partial discharge pattern recognition caused by imbalanced and limited sample sizes, this study proposes a novel approach combining data augmentation with pattern recognition using DAE-GAN. The key findings of the study are summarized as follows:  
1. When assessed using the key criteria of authenticity, sample diversity, and the accuracy with which the model fits the probability distribution, the DAE-GAN model demonstrates a clear advantage in generative capability. It outperforms other models, such as BEGAN, VAE, and CWGAN-GP, in these aspects, making it a more robust choice for generating high-quality samples.
2. After data augmentation with DAE-GAN, the pattern recognition accuracy indicators, \(\lambda_{accuracy}\%\), \(\lambda_{F1}\%\), and \(\lambda_{G-mean}\%\), increased by 8.24%, 9.20%, and 7.69%, respectively, compared to the pattern recognition trained on the original samples. This effectively improves the accuracy of partial discharge pattern recognition. Additionally, the accuracy after DAE-GAN data augmentation is higher compared to other data augmentation algorithms included in the comparison.
3. The sample generation capability of DAE-GAN is enhanced by including complementary samples, effectively addressing the issue of insufficient probabilistic information caused by the small sample characteristics of partial discharge. Compared to when no complementary samples are included, the \(\lambda_{accuracy}\%\), \(\lambda_{F1}\%\), and \(\lambda_{G-mean}\%\) indicators improved by 1.15%, 1.32%, and 1.12%, respectively. The inclusion of complementary samples effectively improves classification performance.  
The current DAE-GAN model still has several areas for improvement, particularly in terms of the collaborative training between the DAE and GAN modules. Further in-depth research is required to effectively coordinate the training processes of both modules to achieve complementary and enhanced performance. Moreover, the hyperparameters of the current model are determined through experimental adjustment, which may not be optimal for different datasets and tasks. Therefore, designing an adaptive mechanism that can dynamically adjust hyperparameters to meet the needs of various training stages, while optimizing the training process and improving overall model performance, remains a crucial challenge to be addressed.  
Conceptualization, J.X.; Data curation, Z.S. and C.W.; Formal analysis, C.W.; Funding acquisition, X.D.; Methodology, X.D. and J.X.; Validation, X.D., J.Q., J.K. and J.X.; Writing--original draft, X.D. All authors have read and agreed to the published version of the manuscript.  
This research received financial support from the Science and Technology Projects of Inner Mongolia Electric Power (Group) Co., Ltd.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
We are thankful to the anonymous reviewers for their insightful comments, which have significantly contributed to improving the quality of this paper. The authors also express their gratitude to North China Electric Power University for their support in the publication of this article. Special thanks are extended to Inner Mongolia Electric Power (Group) Co., Ltd. Alxa Power Supply Branch for their valuable assistance and support during the research process.  
Author [PERSON], [PERSON], [PERSON] were employed by the company Alxa Power Supply Branch, Inner Mongolia Electric Power (Group) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.