广东电力2026，Vol.39Issue(2)：108-119,12.DOI:10.3969/j.issn.1007-290X.2026.02.010
基于深度卷积生成对抗网络与知识蒸馏的聚丙烯电缆缺陷局部放电模式识别方法
Partial Discharge Pattern Recognition Method for Polypropylene Cable Defects Based on DCGAN and Knowledge Distillation
摘要
Abstract
Polypropylene(PP)cables,as a new type of environmentally friendly material,face challenges in safe and stable operation due to partial discharge.To address this issue,this study proposes a pattern recognition method based on deep convolutional generative adversarial networks(DCGAN)and knowledge distillation(KD)techniques.Firstly,four typical PP cable defects were designed,and a 20 kV withstand voltage test was conducted to obtain 400 phase resolved partial discharge(PRPD)data samples.High-quality PRPD image datasets were then expanded using DCGAN.ResNet-110 was employed as the teacher model for training,and the learned feature knowledge was transferred to the lightweight student model ResNet-20 through KD.Experimental results show that the Frechet inception distance(FID)metric between DCGAN-generated images and original samples reaches as low as 13.22,indicating high similarity.With the introduction of KD,the student network achieves a 63.25％reduction in model parameters while maintaining a classification accuracy of 91.25％and improving inference speed by 4.16 times.The results demonstrate that the proposed recognition for PP cables but also significantly enhances model efficiency,providing theoretical and technical support for intelligent cable fault diagnosis.关键词
聚丙烯电缆/局部放电/相位分辩的局部放电/深度卷积生成对抗网络/知识蒸馏/模式识别Key words
polypropylene cable/partial discharge/phase resolved partial discharge(PRPD)/deep convolutional generative adversarial network(DCGAN)/knowledge distillation/pattern recognition分类
信息技术与安全科学引用本文复制引用
吴吉,贾诗媛,李银格,彭小圣,范亚洲..基于深度卷积生成对抗网络与知识蒸馏的聚丙烯电缆缺陷局部放电模式识别方法[J].广东电力,2026,39(2):108-119,12.基金项目
中国南方电网有限责任公司科技项目(GDKJXM20231054) （GDKJXM20231054）
国家自然科学基金面上基金项目(52177146) （52177146）
