作者：邓高峰1, 赵震宇1, 王珺1, 严勤2, 李赫3
作者单位：1. 国网江西省电力有限公司电力科学研究院，江西 南昌 330096;
2. 国网江西省电力有限公司，江西南昌 330096;
3. 南昌科晨电力试验研究有限公司，江西 南昌 330096
关键词：高级计量架构;窃电用户检测;自编码器;随机森林
摘要：
作为智能电网的关键技术之一，高级计量架构凭借实时双向通信、按需应答等优点为电网提供重要的数据来源。面对当前日趋严重的窃电问题，有必要利用高级计量架构的数据发现非法窃电行为。因此，该文提出一种基于改进自编码器和随机森林的窃电嫌疑用户检测方法。通过改进自编码器提取隐含在电力用户用电量信息中的特征，应用批标准化算法优化训练过程，并采用这些特征来构建随机森林模型判断窃电嫌疑用户。运用真实数据集，通过仿真实验并对比现有的BP神经网络、极限学习机等模型验证所提出方法的有效性和准确性。
Detection method for electricity theft based on improved autoencoder and random forest
DENG Gaofeng1, ZHAO Zhenyu1, WANG Jun1, YAN Qin2, LI He3
1. State Grid Jiangxi Electric Power Co., Ltd., Electric Power Research Institute, Nanchang 330096, China;
2. State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, China;
3. Nanchang Kechen Electric Power Test Research Co., Ltd., Nanchang 330096, China
Abstract: As one of the key technologies of smart grid, advanced metering infrastructure provides an important data source for the grid by the advantage of real-time two-way communication and on-demand response. As the increasingly serious of power theft problems, it is necessary to utilize the data of advanced metering infrastructure to find the illegal consumers. Therefore, this paper proposes a method for detecting suspected power theft users based on an improved autoencoder and random forest. By improving the self-encoder to extract the characteristics implicit in the power consumption information of power users, a batch of standardized algorithms is used to optimize the training process, and these characteristics are used to construct a random forest model to determine suspected power theft users. The real data set is used to verify the effectiveness and accuracy of the proposed method through simulation experiments and comparison with existing BP neural network, extreme learning machine and other models.
Keywords: advanced metering infrastructure;electricity theft users detection;autoencoder;random forest
2020, 46(7):83-89 收稿日期: 2020-03-02;收到修改稿日期: 2020-03-31
基金项目: 国家电网科技资助项目（52182019000H）
作者简介: 邓高峰(1987-),男,江西南昌市人,高级工程师,硕士,研究方向为电能计量、计量器具检测技术
参考文献
[1] 田野, 张程, 毛昕儒, 等. 运用PCA改进BP神经网络的用电异常行为检测[J]. 自然科学, 2017, 31(8): 125-133
[2] 刘文松, 刘韶华, 成海生. 面向智能电网的高级计量架构AMI的研究[J]. 电网与清洁能源, 2011, 27(10): 8-12
[3] 曹敏, 邹京希, 魏龄, 等. 基于RBF神经网络的配电网窃电行为检测[J]. 自然科学版, 2018, 40(5): 872-878
[4] CODY C, FORD V, SIRAJ A, et al. Decision tree learning for fraud detection in consumer energy consumption[C]//14th IEEE International Conference on Machine Learning and Applications, 2015.
[5] NABIL M, ISMAIL M, MAHMOUD M, et al. Deep recurrent electricity theft detection in AMI networks with random tuning of hyper-parameters[C]//2018 24th International Conference on Pattern Recognition, 2018.
[6] NAGI J, YAP K S, TIONG S K, et al. Detection of abnormalities and electricity theft using genetic support vector machines[C]//2008 Ieee Region 10 Conference, 2008.
[7] GUERRERO J I, LEON C, MONEDERO I, et al. Improving knowledge-based systems with statistical techniques, text mining, and neural networks for non-technical loss detection[J]. Knowledge-Based Systems, 2014, 71: 376-388
[8] WANG X, YANG I, AHN S H. Sample efficient home power anomaly detection in real time using semi-supervised learning[J]. Ieee Access, 2019, 7: 139712-139725
[9] 庄池杰, 张斌, 胡军, 等. 基于无监督学习的电力用户异常用电模式检测[J]. 中国电机工程学报, 2016, 36(2): 379-387
[10] NIZAR A H, DONG Z Y, WANG Y. Power utility nontechnical loss analysis with extreme learning machine method[J]. Ieee Transactions on Power Systems, 2008, 23(3): 946-955
[11] MOCANU E, NGUYEN P H, GIBESCU M, et al. Deep learning for estimating building energy consumption[J]. Sustainable Energy Grids & Networks, 2016, 6: 91-99
[12] FENZA G, GALLO M, LOIA V. Drift-aware methodology for anomaly detection in smart grid[J]. Ieee Access, 2019, 7: 9645-9657
[13] ZHENG Z, YANG Y, NIU X, et al. Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids[J]. Ieee Transactions on Industrial Informatics, 2018, 14(4): 1606-1615
[14] LO C H, ANSARI N. Consumer: a novel hybrid intrusion detection system for distribution networks in smart grid[J]. Ieee Transactions on Emerging Topics in Computing, 2013, 1(1): 33-44
[15] AHMAD T, CHEN H, WANG J, et al. Review of various modeling techniques for the detection of electricity theft in smart grid environment[J]. Renewable & Sustainable Energy Reviews, 2018, 82: 2916-2933
[16] GLAUNER P, MEIRA J A, VALTCHEV P, et al. The challenge of non-technical loss detection using artificial intelligence: a survey[J]. International Journal of Computational Intelligence Systems, 2017, 10(1): 760-775
[17] VIEGAS J L, ESTEVES P R, MELICIO R, et al. Solutions for detection of non-technical losses in the electricity grid: a review[J]. Renewable & Sustainable Energy Reviews, 2017, 80: 1256-1268
[18] 陈启鑫, 郑可迪, 康重庆, 等. 异常用电的检测方法: 评述与展望[J]. 电力系统自动化, 2018, 42(17): 189-199
[19] JOKAR P, ARIANPOO N, LEUNG V C M. Electricity theft detection in AMI using customers' consumption patterns[J]. Ieee Transactions on Smart Grid, 2016, 7(1): 216-226
[20] 张西宁, 向宙, 夏心锐, 等. 堆叠自编码网络性能优化及其在滚动轴承故障诊断中的应用[J]. 西安交通大学学报, 2018, 52(10): 49-56
[21] 李晓彬, 牛玉广, 葛维春, 等. 基于改进堆叠自编码网络的电站辅机故障预警[J]. 仪器仪表学报, 2019, 40(7): 55-63
[22] 李传煌, 吴艳, 钱正哲, 等. SDN下基于深度学习混合模型的DDoS攻击检测与防御[J]. 通信学报, 2018, 39(7): 176
