基于自适应容积卡尔曼滤波的主动配电网状态估计

有效的状态估计算法是确保电力系统安全、稳定、经济运行的前提条件。针对传统无迹卡尔曼滤波(Unscented Kalman Filter,UKF)参数选取难、灵活性差、高阶系统滤波精度低等缺陷,将数值稳定性较好的容积卡尔曼滤波(Cubature Kalman Filter,CKF)算法引入到配电网进行动态状态估计,并与改进后的自适应无迹卡尔曼滤波(Adaptive Unscented Kalman Filter,AUKF)算法进行对比,仿真分析表明CKF算法较AUKF算法具有较高的滤波精度以及较好的数值稳定性。该算法在系统负荷发生突变时滤波精度有所下降,为此进一步提出了自适应容积卡尔曼滤波(Adaptive Cubature Kalman Filter,ACKF)算法以改善状态估计性能。对三相不平衡电网进行算例仿真表明:ACKF算法相比较于CKF算法,滤波精度更高、鲁棒性更强。

State Estimation of Active Distribution Network Based on ACKF

An effective state estimation algorithm which provides a precondition for ensuring a safe, stable, and economic operation of the power system. Aiming at the shortcomings of the traditional unscented Kalman filter (UKF), such as the difficult question of choosing parameter, less flexibility and filter degradation of high order system. The cubature Kalman filter (CKF) with better numerical stability is introduced into distribution network dynamic state estimation. Compared with the improved adaptive unscented Kalman filter (AUKF) the simulation which show that CKF algorithm has higher filtering accuracy and better numerical stability than the AUKF algorithm. However, When the system load is abrupt, the filtering accuracy decreases. Therefore, in order to improve the estimation performance, an adaptive cubature Kalman filter (ACKF) algorithm is proposed. The simulation analysis is carried out in three-phase unbalanced distribution network which show that the AUKF algorithm has higher filtering accuracy and better numerical stability than the CKF algorithm.