湖南大学学报（自然科学版）2024，Vol.51Issue(6)：211-222,12.DOI:10.16339/j.cnki.hdxbzkb.2024241
基于CT-GAN的半监督学习窃电检测方法研究
Research on Semi-supervised Learning Detection Method of Electricity Theft Based on CT-GAN
摘要
Abstract
Aiming at the high cost and difficulty of obtaining labeled data for power grid companies,and the difficulty of training an effective electricity theft detection model with unlabeled data,this paper proposes a method based on CT-GAN(Co-training Generative Adversarial Networks)semi-supervised electricity theft detection method.Firstly,the principles and structures of generative adversarial networks and semi-supervised generative adversarial networks are explored.Secondly,it is proposed to replace the JS(Jensen-Shannon)divergence and KL(Kullback-Leibler)divergence distance with the Wasserstein distance to solve the problem of unstable model training and low quality of generated data caused by the gradient disappearance and mode collapse of the generative confrontation network problem,and built a multi-discriminator Co-training model to avoid the problem of high distribution error of a single discriminator.At the same time,it enhanced the ability of GAN to generate label sample data.By expanding the label sample data set,the model detection accuracy and generalization ability were improved.Finally,the accuracy and effectiveness of the method are verified using the Irish power grid dataset.关键词
窃电检测/生成对抗网络/半监督学习/Wasserstein距离/判别器Key words
electricity theft detection/generative adversarial network/semi-supervised learning/Wasserstein distance/discriminator分类
信息技术与安全科学引用本文复制引用
杨艺宁,张蓬鹤,夏睿,高云鹏,王飞,朗珍白桑..基于CT-GAN的半监督学习窃电检测方法研究[J].湖南大学学报（自然科学版）,2024,51(6):211-222,12.基金项目
中国电力科学研究院研究开发项目(JL8422-003),Research and Development Project of China Electric Power Research Institute(JL8422-003) （JL8422-003）
