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Research on Partial Discharge Image Recognition of Onboard Cable Terminal Based on Generative Adversarial Network
ID：99 View Protection：ATTENDEE Updated Time：2024-08-15 10:50:13 Hits：362 Oral Presentation

Start Time：Pending（Asia/Shanghai）

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Abstract
This paper addresses the challenges of difficult data acquisition and limited samples of partial discharge (PD) data from onboard cable terminals, which affect the accuracy of insulation defect classification. We propose an enhancement method for PD data from onboard cable terminals based on Generative Adversarial Network (GAN). Using Boundary Equilibrium GAN (BEGAN), Deep Convolutional GAN (DCGAN), and Wasserstein GAN with Gradient Penalty (WGAN-GP) to learn from PD image samples of onboard cable terminals, we evaluate the image generation quality of the three networks. The results show that all three networks have good generation effects on the PD images. We utilize BEGAN, DCGAN, and WGAN-GP to enhance the few-shot PD image dataset of onboard cable terminals and conduct classification training on the enhanced dataset. The results indicate that the enhanced datasets from all three generative networks can improve the accuracy of image classification, with DCGAN demonstrating the best enhancement effect.
 
Keywords
onboard cable terminal,partial discharge,GAN,image recognition,few-shot
Speaker
Jiang Weihui
doctoral student Southwest Jiaotong University

Submission Author
Lijun Zhou Southwest Jiaotong University
Hanqing Ma Southwest Jiaotong University
Weihui Jiang Southwest Jiaotong University
Zhengjia Li Hunan Hengxin Electric Co., Ltd.
Gangyang Zhu Hunan Hengxin Electric Co., Ltd.
Peng Xiong Hunan Hengxin Electric Co., Ltd.
Shuguang He Hunan Hengxin Electric Co., Ltd.
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Important Date

  • Conference Date

    Nov 06

    2024

    to

    Nov 08

    2024

  • Sep 15 2024

    Draft paper submission deadline

  • Nov 08 2024

    Registration deadline

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Huazhong University of Science and Technology
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