基于无迹粒子滤波的配电网状态估计

State estimation of distribution network based on unscented particle filter

配电网状态估计是配电管理系统的重要组成部分。用于状态估计的数据通常存在不同程度的随机噪声干扰,不能直接用于配电网的运行分析,为获得更为精确的配电网状态信息,必须对量测数据进行滤波处理。针对无迹卡尔曼滤波(Unscented Kalman Filter,UKF)灵活性差、滤波精度易受参数及滤波初值的制约;标准粒子滤波(Particle Filter,PF)选取重要性密度函数不合理的缺陷,文章将无迹粒子滤波(Unscented Particle Filter,UPF)算法应用于配电网状态估计。该算法将UKF和PF融合,用UKF结合最新的量测信息为PF生成重要性密度函数,将落在先验概率密度区域的粒子转移到高似然区域内,提高了PF的滤波性能。通过IEEE 33节点系统算例分析,结果表明,UPF较UKF和PF具有更好的估计性能,且灵活性强,是一种有效的状态估计方法。

State estimation of distribution network is an important part of distribution management system.The data which was used for state estimation usually has random noise interference of different degrees and cannot be used for the operation analysis of distribution network directly.In order to obtain more accurate state information of distribution network,the measured data must be filtered.Aiming at the poor flexibility of unscented Kalman filter(UKF),the filtering accuracy is restricted by parameters and initial filtering values easily,and the importance density function selected by the standard particle filter(PF)is unreasonable,and the unscented particle filter(UPF)algorithm is applied to the state estimation of distribution network in this paper.The algorithm combines UKF and PF,and combines UKF with the latest measurement information to generate the importance density function for PF.It transfers the particles falling in the prior probability density region to the high likelihood region.Then,the filtering performance of PF is improved.The results of IEEE 33-node system show that UPF has better performance and flexibility than UKF and PF,and is an effective state estimation method.