报告详情
Research on Partial Discharge Image Recognition of Onboard Cable Terminal Based on Generative Adversarial Network
编号：99
访问权限：仅限参会人
更新：2024-08-15 10:50:13 浏览：144次
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摘要
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.
关键词
onboard cable terminal,partial discharge,GAN,image recognition,few-shot
报告人
Jiang Weihui
doctoral student Southwest Jiaotong University稿件作者
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重要日期
-
会议日期
11月06日
2024
至11月08日
2024
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09月15日 2024
初稿截稿日期
-
11月08日 2024
注册截止日期
主办单位
Huazhong University of Science and Technology
联系方式
历届会议
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2023年12月08日 中国 Chengdu
4th China International Youth Conference on Electrical Engineering -
2023年07月11日 中国 Chengdu
2nd China International Youth Conference on Electrical Engineering -
2022年11月03日 中国 Wuhan
2022 IEEE 3rd China International Youth Conference on Electrical Engineering -
2020年11月02日 中国 Wuhan
China International Youth Conference on Electrical Engineering
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