摘要
窃电行为严重危害着电力设备和人身安全,并造成重大经济损失.对窃电行为实现准确识别是供电企业降损增效的一项重要工作.在残差网络(residual network, ResNet)结构的基础上,将二维卷积神经网络与深度可分离卷积增强的自注意力(depthwise separable convolution enhanced self attention,DSCAttention)机制相结合并构建模型,用于提升窃电用户的正确分类.此外,由于窃电数据常存在缺失值、异常值和正负样本不平衡的问题,故采用补零法、分位数变换和分层拆分法对以上问题分别处理.在真实窃电数据集上进行了大量实验,实验结果表明,所提模型的AUC指标达到了91.92%, MAP@100指标达到了98.58%, MAP@200指标达到了96.77%.与其他窃电分类模型相比,所提模型在窃电分类任务上亦有很好的表现,可以在窃电智能化识别中推广使用.
Power theft seriously endangers power equipment and personal safety,and causes significant economic losses for energy suppliers.Hence,it is important for these suppliers to accurately identify instances of power theft to reduce losses and increase efficiency.In this paper,based on the residual network(ResNet)structure,a 2D convolutional neural network is combined with a depthwise separable convolution enhanced self-attentive(DSCAttention)mechanism to improve the number of correctlyclassified electricity theft users.In addition,electricity theft data often contains missing values,outliers,and positive and negative sample imbalance.Each of the above problems are treated separately using the zero-completion method,quantile transformation,and hierarchical splitting method,respectively.The proposed model has been extensively tested using real power theft data sets.The results show that the area under curve(AUC)index of the proposed model reaches a value of 91.92%,while mean average precision values MAP@100 and MAP@200 are measured reaching 98.58%and 96.77%,respectively.Compared with other electricity theft classification models,the proposed model performs the electricity theft classification task better.The method in this paper can be extended to electricity theft intelligent identification.
作者
段志尚
冉懿
吕笃良
祁杰
钟佳晨
袁培森
DUAN Zhishang;RAN Yi;LYU Duliang;QI Jie;ZHONG Jiachen;YUAN Peisen(The Marketing Service Center(Capital Pooling Centers,Metering Centers),State Grid Xinjiang Electric Power Co.Ltd.,Urumqi,830000,China;College of Artificial Intelligence,Nanjing Agricultural University,Nanjing 210031,China)
出处
《华东师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2023年第5期193-204,共12页
Journal of East China Normal University(Natural Science)
基金
国家自然科学基金(61877018)
上海市大数据管理系统工程研究中心开放基金(HYSY21022)。
关键词
残差网络
卷积增强
自注意力机制
深度可分离卷积
窃电识别
residual network
convolution enhancement
self-attention mechanism
depthwise separable convolution
identifying electricity theft
