A Robust H∞CKF-Based Dynamic State Estimation Method for Distribution Networks

Due to the development of new power systems, the impact of stochastic loads, demand response participation, distributed voltage randomness and volatility and the variety of measurement devices lead to the complexity of the distribution network structure and the aggravation of the state estimation task, which may lead to a decrease in the estimation accuracy of the dynamic state estimation algorithm in some scenarios. In this paper, the dynamic state estimation method for distribution networks based on improved H∞ volumetric Kalman filtering is firstly combined with volumetric Kalman filtering and H∞ filtering to robust the model error uncertainty problem, and then finally combined with a noise valuer to estimate the parameters in the process noise online and to reduce the impact of noise on the prediction error. Simulations are carried out by the IEEE69-node system, and the results show that the method maintains a relatively high estimation accuracy under normal system operation, after the demand response is involved in peak shaving, and when the load undergoes sudden changes.