Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 790-794.doi: 10.11896/jsjkx.210800032

• Interdiscipline & Application • Previous Articles     Next Articles

Application of Grassberger Entropy Random Forest to Power-stealing Behavior Detection

QUE Hua-kun1, FENG Xiao-feng1, LIU Pan-long2, GUO Wen-chong1, LI Jian1, ZENG Wei-liang2, FAN Jing-min2   

  1. 1 Metrology Center of Guangdong Power Grid Corporation,Guangzhou 518049,China
    2 School of Automation,Guangdong University of Technology,Guangzhou 510006,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:QUE Hua-kun,born in 1986,senior engineer.His main research interests include metering automation and charging strategy.
    ZENG Wei-liang,born in 1986,Ph.D,associate professor.His main research interests include routing problem in complex network,traffic simulation and big data visualization for smart city.
  • Supported by:
    Science and Technology Project of China Southern Power Grid Co. Ltd(GDKJXM20185800) and National NaturalScience Foundation of China(61803100).

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Abstract: Power stealing seriously endangers the grid security.In order to improve the efficiency of electricity theft detection,this paper proposes a novel method for electricity stealing detection based on Grassberger entropy random forest.First,KPCA is applied to reduce the dimensionality of the original power time series for extracting the user power consumption characteristics.Then,considering the unbalance of the number of theft samples and normal samples,the data under sampling method is used to establish multiple quantitatively balanced sample subsets.The random forest with improved Grassberger entropy is used tocompute informantion gain,so as to improve the accuracy of the model in power theft detection.Finally,the electricity consumption dataset of China Southern Power Grid is used to verify the power stealing detection effect of the proposed model.

Key words: Grassberger entropy, Kernel principal component analysis, Power stealing detection, Random Forest

CLC Number: 

  • F407.6
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