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Algorithms
Volume 17
Issue 11
10.3390/a17110487
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Article

Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN †

by
Xin Du
1,
Jun Qi
1,
Jiyi Kang
1,
Zezhong Sun
2,
Chunxin Wang
2 and
Jun Xie
2,*
1
Alxa Power Supply Branch, Inner Mongolia Electric Power (Group) Co., Ltd., Alxa 750300, China
2
State Key Laboratory of New Energy Power System, North China Electric Power University, Baoding 071000, China
*
Author to whom correspondence should be addressed.
†
This article is a revised and expanded version of a paper entitled Partial Discharge Data Augmentation and Pattern Recognition for Unbalanced and Small Sample Scenarios, which was presented at the 7th International Seminar on Computer Technology, Mechanical and Electrical Engineering (ISCME 2022) (Hangzhou, China).
Algorithms 2024, 17(11), 487; https://doi.org/10.3390/a17110487
Submission received: 29 August 2024 / Revised: 17 October 2024 / Accepted: 28 October 2024 / Published: 1 November 2024
(This article belongs to the Special Issue AI and Computational Methods in Engineering and Science)
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Abstract

Accurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network (DAE-GAN) is proposed. First, the Deep Autoencoder (DAE) is embedded within the Generative Adversarial Network (GAN) to improve the realism of generated samples. Then, complementary PD data samples are introduced during GAN training to address the issue of limited sample size. Lastly, the model’s discriminator is fine-tuned with augmented and balanced training data to enable PD pattern recognition. The DAE-GAN method is used to augment data and recognize patterns in experimental PD signals. The results demonstrate that, under imbalanced and small sample conditions, DAE-GAN generates more authentic PD samples with improved probability distribution fitting compared to other algorithms, leading to varying levels of enhancement in pattern recognition accuracy.
Keywords: partial discharge; data augmentation; DAE; GAN; DAE-GAN partial discharge; data augmentation; DAE; GAN; DAE-GAN

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MDPI and ACS Style

Du, X.; Qi, J.; Kang, J.; Sun, Z.; Wang, C.; Xie, J. Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN. Algorithms 2024, 17, 487. https://doi.org/10.3390/a17110487

AMA Style

Du X, Qi J, Kang J, Sun Z, Wang C, Xie J. Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN. Algorithms. 2024; 17(11):487. https://doi.org/10.3390/a17110487

Chicago/Turabian Style

Du, Xin, Jun Qi, Jiyi Kang, Zezhong Sun, Chunxin Wang, and Jun Xie. 2024. "Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN" Algorithms 17, no. 11: 487. https://doi.org/10.3390/a17110487

APA Style

Du, X., Qi, J., Kang, J., Sun, Z., Wang, C., & Xie, J. (2024). Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN. Algorithms, 17(11), 487. https://doi.org/10.3390/a17110487

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