基于粒子群算法的低压用户超容用电优化研判策略

随着我国的经济不断发展，电网规模逐渐扩大，面临的挑战也逐渐增加。电网末端极容易出现低电压和用户超容用电等问题，低压用户超容用电会严重影响局部电力系统稳定性，容易造成线损增加甚至威胁电网安全。本文提出了一种基于粒子群算法的低压用户超容用电优化研判策略，建立了用户特征优化模型，通过分析海量用户数据中出现的异常情况来准确判断用户是否为低压超容用户，同时针对海量低压超容用户研判的常规流程进行改进，提出了优化研判策略。算例结果表明，本文所述方法能够从海量用户中准确研判低压超容用户，并且较人工审核方式提高了2.86%的判断准确率。

Optimization strategy for judging excessive use of power by low-voltage users based on particle swarm optimization algorithm

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.