基于粒子群算法的低压用户超容用电优化研判策略

Optimization Strategy for Judging Excessive Use of Power by Low-Voltage Users Based on Particle Swarm Optimization Algorithm

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。

With the continuous economic development in China,the scale of the power grid is expanding,accompanied by increasing challenges.At the end of the power grid,problems such as low voltage and user overloading frequently occur.Overloading by low-voltage users can severely affect the stability of local power systems,leading to increased line losses and even threatening the security of the power grid.This paper proposes an optimization and judgment strategy for identifying low-voltage user overloading based on the particle swarm algorithm,It establishes an optimized model for user characteristics and accurately determines whether a user is a low-voltage overloading user by analyzing abnormal situations in a massive amount of user data.Furthermore,the conventional process for identifying a large number of low-voltage overloading users is improved,and an optimized judgment strategy is proposed.The numerical results demonstrate that the proposed method can accurately identify low-voltage overloading users from a massive user dataset and achieve a 2.86%improvement in accuracy compared to manual auditing methods.