Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (5): 29-36.doi: 10.6040/j.issn.1672-3961.0.2019.116

• Engineering—Special Topic on Artificial Intelligence Application • Previous Articles     Next Articles

None-consumption users filtering algorithm based on decision tree and data-driven methods

Bo ZHANG1(),Feng LU2,Hanyu DONG2,Qingtai CHEN3,Zhenzhi LIN1,4,*(),Hongtao WANG4   

  1. 1. School of Electrical Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
    2. Huzhou Power Supply Company, State Gird Zhejiang Electric Power Company, Huzhou 313000, Zhejiang, China
    3. Zhejiang Huayun Information Technology Co., Ltd., Hangzhou 310012, Zhejiang, China
    4. School of Electrical Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2019-03-25 Online:2019-10-20 Published:2019-10-18
  • Contact: Zhenzhi LIN E-mail:zhbzju@163.com;linzhenzhi@zju.edu.cn
  • Supported by:
    国家重点研发计划(2016YFB0901100);国家自然科学基金资助项目(51777185)

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Abstract:

With the increasing of power consumers and diversification of power consumption in power systems, the number of none-consumption users (NCUs) was also increasing rapidly. Thus, lots of manpower and material resources of power supply companies were arranged to perform troubleshooting on NCUs. Given this background, a data-driven method based on the electricity information of NCUs collected by electricity information acquisition system (EIAS) was proposed to determine the filtering results of normal NCUs and abnormal NCUs. The decision tree was utilized to analyze the electricity data of NCUs, and determine the types of NCUs. The key factors suitable for NCUs filtering were determined based on the original data to filter the NCUs that could not be screened by the decision tree, and the evaluation system for NCUs filtering was constructed. On this basis, CRITIC and radar chart methods were adopted to determine the weights of the key factors and to determine the filtering results of NCUs, respectively. The NCUs power-supplied by an actual power supply station in Zhejiang Province were served for demonstrating the proposed algorithm of NCUs filtering, and the simulation and on-site inspection results showed that the proposed data-driven method was effective for screening out the abnormal NCUs.

Key words: none-consumption user, troubleshoot priority, decision tree, CRITIC method, radar chart method

CLC Number: 

  • TM933.4

Fig.1

Preliminary screening of NCU types based ondecision tree"

Fig.2

Radar chart of the kth NCU"

Table 1

Key factors and filtering results for part of NCUs"

户号 居民类型 合同容量/(kVA) 电压等级/V 信用等级 异常事件/件 环比电量/(kW·h) 同比电量/(kW·h) 离散系数/(p.u.) 台区线损变化率/(p.u.) 评分/(p.u.)
****050083 城镇居民 4 380 最高信用等级 4 0.00 2 808.39 0.92 0.02 57.61
****051024 城镇居民 45 380 最高信用等级 4 119.00 13 234.20 0.94 0.11 67.12
****019099 乡村居民 6 220 - 4 0.00 899.79 1.44 0.14 39.15
****020500 乡村居民 6 220 - 4 4.85 394.30 1.84 0.13 39.15
****021224 乡村居民 6 220 - 4 0.00 393.31 1.09 0.02 38.80
… … … … … … … … … … …
****063612 城镇居民 10 380 - 4 0.00 598.80 1.24 0.05 96.55
****064572 乡村居民 8 220 - 4 0.00 982.82 1.27 0.17 39.38
****064986 城镇居民 20 380 - 4 0.00 681.71 1.33 0.07 97.95
****064987 城镇居民 20 380 - 4 0.00 7 197.39 0.84 0.01 100.00

Fig.3

Weights of key factors of troubleshootingpriority for NCUs"

Fig.4

The radar chart of the highest and lowest rated NCUs"

Fig.5

The daily electricity consumption curve of the highest and lowest rated NCUs"

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