## 1 Introduction  
In order to obtain the optimal control strategy of generator in power system, phasor measurement unit (PMU) is required to observe the generator data (power angle, electric angular velocity, etc.) to determine its state. However, there are random noises and measurement errors in PMU measurements, which cannot be directly used as the true state value [1; 2; 3]. Statistical method is used to calculate the estimated value of generator state variables and predict the future state. This method is called dynamic state estimation (DSE). Therefore, the prediction of generator's future state by DSE is helpful for power system regulation and has important significance to the stability of power network. In recent years, the problem of electromechanical transient state estimation of generators has attracted wide attentions. Generator is a complex nonlinear system, and its DSE needs corresponding nonlinear filtering algorithm. In the field of generator DSE algorithms, many studies mainly focus on extended Kalman filter (EKF), unscented Kalman filter (UKF) and cubature Kalman filter (CKF) [4; 5]. In [6], EKF was implemented to estimate the dynamic state of generator, where the fourth-order model of generator with unknown input was utilized. However, the linearization process of the generator model by EKF will lead to large truncation errors, which cannot be applied to the strongly nonlinear systems. In [7; 8], the deterministic sampling filter-UKF algorithm was applied to estimate the dynamic state of generator. Due to the poor flexibility of UKF in parameter selection, the CKF method was proposed by [PERSON] [9; 10]. In [11], the CKF was used for the DSE of generator, which could achieve much better performance than both EKF and UKF. On the other hand, considering the system uncertainties, in [12], based on the second-order model of generator, adaptive interpolation and \(H_{\infty}\) theory was introduced into EKF to estimate the dynamic state of generator, by which the robustness was improved. In [13], an online state estimation method for synchronous generators based on UKF was proposed, and the fourth-order nonlinear model of generators was used to verify that the algorithm has not only high filtering speed and accuracy but also strong robustness. In [14], square root filtering was introduced into CKF to ensure the nonnegative qualitative and numerical stability of covariance matrix, by which, the asymmetric or nonnegative covariance of CKF in the iteration of DSE could be avoided and the estimation accuracy of CKF could also be improved. To the best of the authors' knowledge, there have been few results concerning DSE for generator by using ensemble Kalman filtering algorithm [15], which has been widely utilized in meteorology. In this paper, based on the improved EnKF-EnSRF, which can approximate the nonlinear system by random sampling method, an adaptive ensemble root mean square Kalman filter algorithm is proposed. The proposed method does not need to interfere with the measured values, avoiding the problem of underestimating the analysis error covariance in EnKF and improving the filtering precision. Meanwhile, the Sage-Husa algorithm is introduced to estimate measurement noise online. When there is a deviation in measurement noise, the method can revise the covariance of measurement noise dynamically and filter it, which reduces the influence of noise error on filtering. Extensive simulation results show that the proposed method can not only achieve a higher filtering precision than the conventional EnKF but also with a stronger robustness to noise. Hence, the main contributions of this paper are:  
* Proposing a novel, robust AEnSRF method applicable for measurement noise estimation. * The proposed robust AEnSRF method is based on the combination between the EnSRF and the Sage-Husa algorithm. * The proposed AEnSRF does not need to interfere with the measured values, avoiding the problem of underestimating the analysis error covariance in EnKF and improving the filtering precision.