Robust State Estimation Method for Power System Based on Maximum Exponential Square and Data Fusion

Abstract

In order to solve the problem of the time offset between the supervisory control and data acquisition system and phasor measurement unit and the unknown distribution of non-Gauss noise, this paper proposes a robust state estimation method for power systems based on the Maximum Exponential Square and data fusion. Firstly, the robust Mahalanobis distance is used to detect system outliers and assign appropriate weights to the selected PMU buffer measurement. Then, the MES-based estimator uses these weights to filter out non-Gauss PMU measurement noise to generate a set of state estimation results. At the same time, the MES estimator is used to process the received SCADA measurement with unknown measurement noise, thereby generating another set of state estimation results. Finally, the two sets of estimation results from two independent MES estimators are fused by using data fusion theory to obtain the final optimal state estimation results. Based on IEEE-14 and 30-buses standard system, the simulation results prove the effectiveness and robustness of the method proposed in this paper.