With the development of the smart grid

the distribution system operation conditions become more complex and changeable. Furthermore

due to the influence of observation outliers and uncertain noise statistics

it is more difficult to grasp the dynamic operation characteristics of distribution system. In order to address these problems

by using projection statistics and the noise covariance updating technology based on the Sage-Husa noise estimator

for distribution power system with outliers and uncertain noise statistics

a robust adaptive cubature Kalman filter forecasting-aided state estimation method is proposed based on generalized-maximum likelihood type estimator. Furthermore

an adaptive strategy

which can enhance the filtering accuracy under normal conditions

is presented. In the simulation part

the branch parameters and node load parameters of the test system are appropriately modified to simulate the asymmetry of the three-phase branch parameters and the asymmetry of the three-phase loads. Finally

through simulation experiments on the improved test system

it is verified that the robust forecasting-aided state estimation method

presented in this paper

can effectively perceive the actual operating state of the distribution network in different simulation scenarios.