计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 790-794.doi: 10.11896/jsjkx.210800032
阙华坤1, 冯小峰1, 刘盼龙2, 郭文翀1, 李健1, 曾伟良2, 范竞敏2
QUE Hua-kun1, FENG Xiao-feng1, LIU Pan-long2, GUO Wen-chong1, LI Jian1, ZENG Wei-liang2, FAN Jing-min2
摘要: 窃电行为严重危害电网安全,为了提高对窃电行为的检测效率,提出一种新型的基于Grassberger熵随机森林的电网用户窃电检测方法。首先,采用核主成分分析方法(Kernel Principal Componemt Analysis,KPCA)对用户的原始用电量的时间序列向量进行降维,提取用户的用电特征;接着,考虑到窃电样本和正常样本数量相差较大时,窃电检测的分类器训练效果较差,因此,采用数据欠采样方法建立多个数量平衡的样本子集,并采用改进的Grassberger熵随机森林(Random Forest,RF)算法计算信息增益,对各样本子集进行训练再集成,从而提高模型对窃电检测的准确度。以中国南方电网的专变用户窃电检测为案例,将各用户的电表采集电量数据作为模型输入,验证所提模型的窃电检测效果。
中图分类号:
[1] TIAN L,XIANG M.Abnormal Power Consumption Analysis Based on Density-based Spatial Clustering of Applications with Noise in Power Systems[J].Automation of Electric Power Systems,2017,41(5):64-70. [2] WANG G L,ZHOU G L,ZHAO H S,et al.Fast Clustering and Anomaly Detection Technique for Large-scale Power Data Stream[J].Automation of Electric Power Systems,2016,40(24):27-33. [3] FAHIM M,SILLITTI A.Analyzing Load Profiles of EnergyConsumption to Infer.Household Characteristics Using Smart Meters[J].Energies,2019,12:169-173. [4] LEANDRO A P J,CAIO C O R,RODRIGUES D,et al.Unsupervised non-technical losses identification through optimum-path forest[J].Electric Power Systems Research,2016,140:413-423. [5] ZANETTI,M,JAMHOUR E,PELLENZ M,et al.A tunable fraud detection system for advanced metering infrastructure using short-lived patterns[J].IEEE Transactions on Smart grid,2019,10(1):830-840. [6] MUNIZ C,FIGUEIREDO K,VELLASCO M,et al.Irregularity detection on low tension electric installations by neural network ensembles[C]//2009 International Joint Conference on Neural Networks.IEEE,2016:2176-2182. [7] COSTA B C,LA ALBERTO B,PORTELA A,et al.Fraud detection in electric power distribution networks using an ann-based knowledge-discovery process[J].International Journal of Artificial Intelligence & Applications,2019,4(6):17. [8] LIN J N,CHENG Z H,LIN B X.Study on identification method of stolen electricity based on MEA-BP[J].Electronic Design Engineering,2021,29(11):175-180. [9] SPIRI'C J V,STANKOVI'C S S,BDOˇCI'C M,et al.Using the rough set theoryto detect fraud committed by electricity custo-mers[J].International Journal of ElectricalPower & Energy Systems,2014,62(1):727-734. [10] YANG X L,TAO X F,XIONG X,et al.Detection Method for Electricity Theft Based on Deep Forest Algorithm[J].Smart Power,2019,47(10):85-92. [11] CAI J H,WANG K,DONG K,et.al.Power user stealing detection based on DenseNet and random forest[J].Journal of Computer Applications,2021,41(S1):75-80. [12] CIESLAK D A,CHAWLA N V,STRIEGEL A.Combating imbalance in network intrusion datasets[C]//IEEE International Conference on Granular Computing.2006:732-737. [13] ZHAO Z X,WANG G L,LI X D.An Improved SVM Based Under-Sampling Method for Classifying Imbalanced Data[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2012,51(6):10-16. [14] YANG J,YAN X F,ZHANG D P.Cost-sensitive Software Defect Prediction Method Based on Boosting[J].Computer Scien-ce,2017,44(8):176-180. [15] LIU X Y,WU J,ZHOU Z H.Exploratory under sampling for class-imbalance learning[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2009,39(2):539-550. [16] CHEN S Z,ZHU J P,YOU T G.Study on Unbalanced Custo-mer Loss Based on SMOTERF Algorithm[J].Journal of Mathematics in Practice and Theory,2019,1(9):204-210. [17] BARUA S,ISLAM M M,YAO X,et al.MWMOTE-majority weighted minority oversampling technique for imbalanced data set learning[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(2):405-425. [18] LIU X Y,WANG S T,ZHANG M L.Transfer synthetic over-sampling for class-imbalance learning with limited minority class data[J].Frontiers of Computer Science,2019,13(5):406-415. [19] DEL RÍO S,LÓPEZ V,BENÍ-TEZ J M,et al.On the use ofMapReduce for imbalanced big data using Random Forest[J].Information Sciences,2014,12(1):235-239. [20] ZHANG M,HU X H,WU J X.Imbalanced Data Processing Algorithm Based on Mixed Sampling[J].Computer Engineering and Applications,2019,55(17):68-75. [21] MA J J,PAN Q,LIANG Y,et al.Object Detection Based on Improved Grassberger Entropy Random Forest Classifier[J].Chinese Journal of Lasers,2019,46(7):238-246. |
[1] | 李其烨, 邢红杰. 基于最大相关熵的KPCA异常检测方法 KPCA Based Novelty Detection Method Using Maximum Correntropy Criterion 计算机科学, 2022, 49(8): 267-272. https://doi.org/10.11896/jsjkx.210700175 |
[2] | 高振卓, 王志海, 刘海洋. 嵌入典型时间序列特征的随机Shapelet森林算法 Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features 计算机科学, 2022, 49(7): 40-49. https://doi.org/10.11896/jsjkx.210700226 |
[3] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
[4] | 王文强, 贾星星, 李朋. 自适应的集成定序算法 Adaptive Ensemble Ordering Algorithm 计算机科学, 2022, 49(6A): 242-246. https://doi.org/10.11896/jsjkx.210200108 |
[5] | 章晓庆, 方建生, 肖尊杰, 陈浜, RisaHIGASHITA, 陈婉, 袁进, 刘江. 基于眼前节相干光断层扫描成像的核性白内障分类算法 Classification Algorithm of Nuclear Cataract Based on Anterior Segment Coherence Tomography Image 计算机科学, 2022, 49(3): 204-210. https://doi.org/10.11896/jsjkx.201100085 |
[6] | 刘振宇, 宋晓莹. 一种可用于分类型属性数据的多变量回归森林 Multivariate Regression Forest for Categorical Attribute Data 计算机科学, 2022, 49(1): 108-114. https://doi.org/10.11896/jsjkx.201200189 |
[7] | 肖丁, 张玙璠, 纪厚业. 基于多头注意力机制的用户窃电行为检测 Electricity Theft Detection Based on Multi-head Attention Mechanism 计算机科学, 2022, 49(1): 140-145. https://doi.org/10.11896/jsjkx.210100177 |
[8] | 杨小琴, 刘国军, 郭建慧, 马文涛. 基于随机森林的空域-频域联合特征全参考彩色图像质量评价方法 Full Reference Color Image Quality Assessment Method Based on Spatial and Frequency Domain Joint Features with Random Forest 计算机科学, 2021, 48(8): 99-105. https://doi.org/10.11896/jsjkx.200700106 |
[9] | 郑建华, 李小敏, 刘双印, 李迪. 融合级联上采样与下采样的改进随机森林不平衡数据分类算法 Improved Random Forest Imbalance Data Classification Algorithm Combining Cascaded Up-sampling and Down-sampling 计算机科学, 2021, 48(7): 145-154. https://doi.org/10.11896/jsjkx.200800120 |
[10] | 李娜娜, 王勇, 周林, 邹春明, 田英杰, 郭乃网. 基于特征重要度二次筛选的DDoS攻击随机森林检测方法 DDoS Attack Random Forest Detection Method Based on Secondary Screening of Feature Importance 计算机科学, 2021, 48(6A): 464-467. https://doi.org/10.11896/jsjkx.200900101 |
[11] | 曹扬晨, 朱国胜, 祁小云, 邹洁. 基于随机森林的入侵检测分类研究 Research on Intrusion Detection Classification Based on Random Forest 计算机科学, 2021, 48(6A): 459-463. https://doi.org/10.11896/jsjkx.200600161 |
[12] | 徐佳庆, 胡小月, 唐付桥, 王强, 何杰. 基于随机森林的高性能互连网络阻塞故障检测 Detecting Blocking Failure in High Performance Interconnection Networks Based on Random Forest 计算机科学, 2021, 48(6): 246-252. https://doi.org/10.11896/jsjkx.201200142 |
[13] | 周益旻, 刘方正, 王勇. 基于混合方法的IPSec VPN加密流量识别 IPSec VPN Encrypted Traffic Identification Based on Hybrid Method 计算机科学, 2021, 48(4): 295-302. https://doi.org/10.11896/jsjkx.200700189 |
[14] | 张天瑞, 魏铭琦, 高秀秀. 基于IPSO-WRF的选择性激光烧结件气泡溶解时间预测模型 Prediction Model of Bubble Dissolution Time in Selective Laser Sintering Based on IPSO-WRF 计算机科学, 2021, 48(11A): 638-643. https://doi.org/10.11896/jsjkx.210300080 |
[15] | 刘振鹏, 苏楠, 秦益文, 卢家欢, 李小菲. FS-CRF:基于特征切分与级联随机森林的异常点检测模型 FS-CRF:Outlier Detection Model Based on Feature Segmentation and Cascaded Random Forest 计算机科学, 2020, 47(8): 185-188. https://doi.org/10.11896/jsjkx.190600162 |
|