Abstract:With the continuous development of my country''s economy, the scale of the power grid has gradually expanded, and the challenges are gradually increasing. The end of the power grid is prone to low voltage and user ultra -capacity power consumption. Ultra -capacity power consumption of low -voltage users will seriously affect the stability of the local power system, which is easy to cause increased line loss or even threatening the security of the grid. This article proposes a research and judgment strategy of low -voltage users with a particle group algorithm -based ultra -capacity power consumption user, and establishes a user feature optimization model. By analyzing the abnormal conditions in massive user data, accurately judge whether the user is a low -voltage ultra -capacity power user user. At the same time, improved the conventional processes of large -volume low -voltage ultra -capacity power users, and proposed an optimized research and judgment strategy. The results of the calculation show that the method described in this article can accurately judge the low -voltage ultra -volume electricity users from massive users, and increase the accuracy of judgment by 2.86%compared with the manual review method.