1State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China
Ruizhi Chen
1State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China
Yulan Shen
1State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, China
Pu Zhang
State Grid Beijing Economic Research Institute, Beijing, China
Zhaoyan Liu
State Grid Beijing Electric Power Company, Beijing, China
Yansheng Lang
China Electric Power Research Institute, Beijing, China
Xiaonan Yang
China Electric Power Research Institute, Beijing, China
In this paper, a novel robust state estimator (RSE) based on rectangular pulse function (RPF) is proposed considering the uncertainty in the measurements, leading an ideal RPF estimator. The goal of the proposed ideal RPF estimator is to find an estimate value of the state variables to maximize the number of normal measurements. Considering that the objective function of the ideal RPF estimator is non-differentiable, a differentiable function formed by hyperbolic tangent function is used to replace the objective function of the ideal RPF estimator, leading a practical RPF estimator which is easy to be solved by the primal-dual interior point method. The robustness and high computational efficiency of the proposed RPF estimator are demonstrated by simulations based on the IEEE benchmark systems.
