Abstract
Among the end-users of the power grid, especially in the rural power grid, there are a large number of users and the situation is complex. In this complex situation, there are more leakage caused by insulation damage and a small number of users stealing electricity. Maintenance staff will take a long time to determine the location of the abnormal user meter box. In view of this situation, the method of subjective fuzzy clustering and quartile difference is adopted to determine the partition threshold. The power consumption data of end-users are divided into three regions: high, normal and low, which can be used to screen users in the area of abnormal power consumption. Then the trend judgment method is used to further accurately screen to improve the accuracy and reduce the number of users in the abnormal range. Finally according to abnormal power consumption auxiliary locate abnormal electricity users list box. Then the simulation environment is set to verify the application of membership fuzzy clustering and trend judgment in power consumption data partition.
Highlights
In the low-voltage distribution network, especially in the rural-urban junction or rural power network, the low-voltage users are many and scattered
Since power network has been used for a long time and the construction is relatively backward, the insulation of some lines is aging, some lines are laid with bare wires, some lines are winding around obstacles, so the fault rate is high
With the help of the trend-aided judgment model, the statistical window period of power load is determined, and the abnormal user meter box is filtered out more accurately according to the fitting slope, so as to improve the accuracy of electricity consumption analysis
Summary
In the low-voltage distribution network, especially in the rural-urban junction or rural power network, the low-voltage users are many and scattered. The data mining technology of electricity consumption can be used to judge users’ behavior of stealing electricity [15–17] This kind of research only uses electricity consumption data to identify electric theft, without considering the identification of local leakage faults that are common in low-voltage distribution networks. The above research did not use the method of user power consumption data partition to simultaneously identify the leakage fault and power theft behavior of low-voltage end users. With the help of the trend-aided judgment model, the statistical window period of power load is determined, and the abnormal user meter box is filtered out more accurately according to the fitting slope, so as to improve the accuracy of electricity consumption analysis. Filter out the required users to form a list of key table boxes
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