Identify power stealing time period based on user power consumption data

Zhi Xie; Jiaju Wang; Chen Liu; Tai Bai; Dake He; Weimin Chen; Yihui Ding; Junyou Shi

doi:10.1117/12.2671811

21 March 2023 Identify power stealing time period based on user power consumption data

Zhi Xie, Jiaju Wang, Chen Liu, Tai Bai, Dake He, Weimin Chen, Yihui Ding, Junyou Shi

Proceedings Volume 12609, International Conference on Computer Application and Information Security (ICCAIS 2022); 126091A (2023) https://doi.org/10.1117/12.2671811
Event: International Conference on Computer Application and Information Security (ICCAIS 2022), 2022, ONLINE, ONLINE

Abstract

In order to recover the loss of the stolen electric power, a comprehensive approach by analyzing the user power consumption data has been developed. According to the characteristics of the power stealing methods of each category of users, a comprehensive algorithm is employed to identify the method and the length of the power stealing behaviors. Abnormal data series that meet the power stealing characteristics for voltage, current, power and power factor are classified. Clustering algorithm is employed to identify abnormal power consumption, such that time period of power stealing can be calculated. Classification and clustering algorithms are synthesized such that results of different algorithms with different principles can be cross-verified, and stolen power can be recovered accurately and fairly.

Citation Download Citation

Zhi Xie, Jiaju Wang, Chen Liu, Tai Bai, Dake He, Weimin Chen, Yihui Ding, and Junyou Shi "Identify power stealing time period based on user power consumption data", Proc. SPIE 12609, International Conference on Computer Application and Information Security (ICCAIS 2022), 126091A (21 March 2023); https://doi.org/10.1117/12.2671811

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