基于粒子群优化方法的电力系统状态向量估计模型

State vector estimation model of power system based on particle swarm optimization

摘要:

传统的基于最小二乘法的状态向量估计方法,存在估计值与实际电力系统中的参数值相差较大的问题,基于此提出了一种适用于电力系统实时监测的有效状态估计模型。该模型采用了一种基于直角坐标系的加权最小二乘法,由一组与测量量和状态变量相关的非线性方程组描述,使用预测-校正迭代技术求解状态估计器模型。利用粒子群算法优化同步相量测量单元(Phasor Measurements Unit,PMU)仪表的分配,增强了算法的有效性。该模型被应用于IEEE14总线和IEEE-30总线测试系统。结果表明,提出模型在收敛迭代次数、执行时间和准确性方面对状态最优估计做出了重要贡献。

Abstract:

The traditional state estimation method based on the least square method has the problem that there is a difference between the estimated value and the actual power system parameter value. Based on this, an effective state estimation model suitable for real-time monitoring of power systems is proposed. The model uses least squares based on a rectangular coordinate system. It is described by a set of nonlinear equations related to measured quantities and state variables. The state estimator model is replaced by a prediction correction technique. The particle swarm optimization algorithm is used to optimize the allocation of synchronous phasor measurement units (phasor measurement units, PMU), thereby improving the effectiveness of the algorithm. This model is called IEEE14 bus and IEEE-30 bus test system. The results show that the proposed model has made important contributions to optimal state estimation in terms of the number of convergence iterations, execution time and accuracy.