基于自适应无迹卡尔曼滤波的配电网状态估计

State estimation of distribution network based on AUKF

随着大量分布式电源和电动汽车接入配电网,DG出力难以预测以及负荷监控复杂是配电网运行管理的难题。针对传统无迹卡尔曼滤波预测误差大,且容易受不良数据影响的问题,利用新息向量构造了自适应因子,提出自适应无迹卡尔曼滤波(Adaptive Unscented Kalman Filter,AUKF)算法对配电网进行状态估计。当系统负荷突变以及量测存在不良数据时,利用自适应因子对相应的预测协方差矩阵进行在线修正,减小了预测误差对估计精度的影响。在三相不平衡配电网中进行仿真分析,结果表明,AUKF算法比UKF估计精度高、鲁棒性强,验证了所提算法的有效性。

With a large number of distributed generation and electric vehicles connected to the distribution network, the unpredictable output of DG and the complex monitoring of load are the difficult problems for the operation and management of distribution network. Considering the questions of large errors and being susceptible to bad data in traditional Unscented Kalman Filter（UKF） prediction, the adaptive factor is constructed by using the new vector and an Adaptive Unscented Kalman Filter（AUKF） algorithm is proposed to state estimation of distribution network. When the system load is abrupt and the measurement has bad data, the corresponding predictor covariance matrix is modified online by the adaptive factor to reduce the influence of prediction error on the estimation accuracy. The simulation analysis is carried out in three-phase unbalanced distribution network. The results show that the AUKF algorithm is more accurate and robust than the UKF estimation, and the validity of presented algorithm is verified.