1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources;(North China Electric Power University), Beijing 102206, China;2.State Grid Xinjiang Electric Power Co., Ltd., Urumqi 830002, China;3.Department of Electrical and Computer Engineering, Mississippi State University, Starkville 39762, USA

In recent years, dynamic state estimation for generators based on synchrophasor measurement unit (PMU) measurement data has attracted more and more attention. However, the PMU operation in the field is affected by many factors, which may cause the generator terminal voltage or current measurement phasors which are the input of the state estimation to have bad data and affect the state estimation results. To solve this problem, a dynamic state estimation method for generators considering the bad data in input is proposed. After analyzing the influence of input bad data on dynamic state estimation, this method uses the characteristic that the generator states are restricted by the dynamic equation and cannot change suddenly, and a state prediction method based on exponential smoothing is proposed. The predicted states are compared with the forecasting states computed by the generator dynamic equation, and a method for detecting bad input data is proposed. Furthermore, when detecting bad data, predicted states by exponential smoothing are used to replace the forecasting states of the dynamic equation, and an input correction method based on the least square method is proposed. The results of simulation examples show that the method can effectively suppress the influence of input bad data on dynamic state estimation for generators and improve the robustness of the dynamic state estimation algorithm.