Robust Forecasting-Aided State Estimation Considering Uncertainty in Distribution System

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.