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

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...

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Bibliographic Details
Published in:Algorithms 2024-11, Vol.17 (11), p.487
Main Authors: Du, Xin, Qi, Jun, Kang, Jiyi, Sun, Zezhong, Wang, Chunxin, Xie, Jun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Authenticity
DAE
DAE-GAN
Data augmentation
Discharge
Electric equipment
Electrical equipment
Electrical equipment and supplies
Electrical machinery
GAN
Generative adversarial networks
Methods
partial discharge
Pattern recognition
Probability distribution
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Summary: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.
ISSN:1999-4893
1999-4893
DOI:10.3390/a17110487