华东师范大学学报（自然科学版） >

2023 , Vol. 2023 >Issue 5: 193 - 204

DOI: https://doi.org/10.3969/j.issn.1000-5641.2023.05.016

数据分析

基于残差网络和深度可分离卷积增强自注意力机制的窃电识别

  • 段志尚 ,
  • 冉懿 ,
  • 吕笃良 ,
  • 祁杰 ,
  • 钟佳晨 ,
  • 袁培森
展开
  • 1. 国网新疆电力有限公司 营销服务中心(资金集约中心、计量中心), 乌鲁木齐, 830000
    2. 南京农业大学 人工智能学院, 南京　210031
段志尚, 男, 硕士, 工程师, 主要研究方向为电能计量、数据分析. E-mail: 15739576170@163.com

收稿日期: 2023-07-05

  网络出版日期: 2023-09-20

基金资助

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)

收起

Identifying electricity theft based on residual network and depthwise separable convolution enhanced self attention

  • Zhishang DUAN ,
  • Yi RAN ,
  • Duliang LYU ,
  • Jie QI ,
  • Jiachen ZHONG ,
  • Peisen YUAN
Expand
  • 1. The Marketing Service Center (Capital Pooling Centers, Metering Centers), State Grid Xinjiang Electric Power Co. Ltd., Urumqi,　830000, China
    2. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing　210031, China

Received date: 2023-07-05

  Online published: 2023-09-20

Fold

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.

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

本文引用格式

段志尚 , 冉懿 , 吕笃良 , 祁杰 , 钟佳晨 , 袁培森 . 基于残差网络和深度可分离卷积增强自注意力机制的窃电识别[J]. 华东师范大学学报（自然科学版）, 2023 , 2023(5) : 193 -204 . DOI: 10.3969/j.issn.1000-5641.2023.05.016

Abstract

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 correctly-classified 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.

Key words： residual network; convolution enhancement; self-attention mechanism; depthwise separable convolution; identifying electricity theft

参考文献

1 XIA R, GAO Y P, ZHU Y Q, et al.. An attention-based wide and deep CNN with dilated convolutions for detecting electricity theft considering imbalanced data. Electric Power Systems Research, 2023, 214 (A): 108886.
2 GLAUNER P, MEIRA J A, VALTCHEV P, et al.. The challenge of non-technical loss detection using artificial intelligence: A survey. International Journal of Computational Intelligence Systems, 2017, 10 (1): 760- 775.
3 BUZAU M M, TEJEDOR-AGUILERA J, CRUZ-ROMERO P, et al.. Detection of non-technical losses using smart meter data and supervised learning. IEEE Transactions on Smart Grid, 2018, 10 (3): 2661- 2670.
4 XIA X F, XIAO Y, LIANG W, et al.. Detection methods in smart meters for electricity thefts: A survey. Proceedings of the IEEE, 2022, 110 (2): 273- 319.
5 YATSKO V A.. Patterns of using the Z-score for text classification purposes . Automatic Documentation and Mathematical Linguistics, 2022, 56 (5): 245- 250.
6 周赣, 华济民, 李铭钧, 等.. 基于图转换和混合卷积神经网络的窃电检测方法. 电力系统自动化, 2022, 46 (19): 78- 86.
7 LI S, HAN Y H, YAO Y, et al. Electricity theft detection in power grids with deep learning and random forests[J]. Journal of Electrical and Computer Engineering, 2019: Article ID 4136874. DOI: 10.1155/2019/4136874.
8 李强, 张立梅, 白牧可.. 基于多元数据特征和改进随机森林的智能配电网异常数据辨识. 科学技术与工程, 2023, 23 (5): 2007- 2015.
9 ROUZBAHANI H M, KARIMIPOUR H, LEI L. An ensemble deep convolutional neural network model for electricity theft detection in smart grids[C]// 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2020: 3637-3642.
10 徐扬, 张耀, 陈宇轩, 等.. 基于Bagging混合策略的多风电场稀疏向量自回归概率预测. 电力系统保护与控制, 2023, 51 (7): 95- 106.
11 谭庆康, 潘沛生.. 基于注意力机制的堆叠LSTM心电预测算法. 计算机技术与发展, 2023, 33 (1): 62- 67.
12 赵星宇, 吴泉军, 朱威.. 基于CEEMDAN和TCN-LSTM模型的短期电力负荷预测. 科学技术与工程, 2023, 23 (4): 1557- 1564.
13 CHEN W, SHI K.. Multi-scale attention convolutional neural network for time series classification. Neural Networks, 2021, 136 (2): 126- 140.
14 HASAN M N, TAMA R N, NAHID A A, et al.. Electricity theft detection in smart grid systems: A CNN-LSTM based approach. Energies, 2019, 12 (17): 3310- 3328.
15 简定辉, 李萍, 黄宇航.. 基于GA-VMD-ResNet-LSTM网络的短期电力负荷预测. 国外电子测量技术, 2022, 41 (10): 15- 22.
16 LI Y X, CAO W C, DROSSOS K, et al. Domestic activity clustering from audio via depthwise separable convolutional autoencoder network[C]// 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2022. DOI: 10.1109/MMSP55362.2022.9949512.
17 WU H X, HU T G, LIU Y, et al. TimesNet: Temporal 2D-variation modeling for general time series analysis [EB/OL]. (2023-04-12)[2023-05-11]. https://doi.org/10.48550/arXiv.2210.02186.
18 ZHENG Z B, YANG Y T, NIU X D, et al.. Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Transactions on Industrial Informatics, 2018, 14 (4): 1606- 1615.
19 WANG Z G, OATES T. Imaging time-series to improve classification and imputation[C]// Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI’15). AAAI, 2015: 3939-3945.
20 赵晋斌, 张建平, 毛玲, 等.. 基于PSO-Soft attention双向LSTM算法的光伏发电量预测研究. 智慧电力, 2022, 50 (3): 1- 7.
21 皇甫晓瑛, 钱惠敏, 黄敏.. 结合注意力机制的深度神经网络综述. 计算机与现代化, 2023, (2): 40- 49.
22 VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17). Red Hook, NY, USA: Curran Associates Inc., 2017: 6000-6010.
23 HEARST M A, DUMAIS S T, OSUNA E, et al.. Support vector machines. IEEE Intelligent Systems and Their Applications, 1998, 13 (4): 18- 28.
Options
文章导航

/

〈 〉