Abstract
Dynamic state estimation (DSE) for generators plays an important role in power system monitoring and control. Phasor measurement unit (PMU) has been widely utilized in DSE since it can acquire real-time synchronous data with high sampling frequency. However, random noise is unavoidable in PMU data, which cannot be directly used as the reference data for power grid dispatching and control. Therefore, the data measured by PMU need to be processed. In this paper, an adaptive ensemble square root Kalman filter (AEnSRF) is proposed, in which the ensemble square root filter (EnSRF) and Sage–Husa algorithm are utilized to estimate measurement noise online. Simulation results obtained by applying the proposed method show that the estimation accuracy of AEnSRF is better than that of ensemble Kalman filter (EnKF), and AEnSRF can track the measurement noise when the measurement noise changes.
Highlights
In order to obtain the optimal control strategy of generator in power system, phasor measurement unit (PMU) is required to observe the generator data to determine its state
In the field of generator Dynamic state estimation (DSE) algorithms, many studies mainly focus on extended Kalman filter (EKF), unscented Kalman filter (UKF) and cubature Kalman filter (CKF) [4,5]
A novel DSE method was proposed based on the ensemble square root filter (EnSRF); a simplified Sage–Husa adaptive
Summary
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. 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 high filtering speed and accuracy and strong robustness. 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. 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.
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