Adaptive-Reset Extended Kalman Filter Method for Subsynchronous Oscillation Monitoring

The proliferation of renewable generations significantly in power system increases the severity and possibility of subsynchronous oscillations (SSOs) recently. Monitoring SSOs is critical for setting up control strategies and mitigating SSOs effectively. In many SSO events, the oscillation frequency and magnitude are time-varying, which brings great challenges to SSO monitoring. This article proposes an adaptive-reset extended Kalman filter (AREKF) method for accurate estimation of SSO modes. Two improvements are made to the EKF method. The first one is establishing a four-state SSO signal model for the EKF algorithm to track damping factors of SSO modes. The second one is developing an adaptive-reset method to make the EKF algorithm capable of handling the time-varying SSOs by resetting the covariance matrix adaptively. The threshold in the adaptive-reset criterion is automatically tuned with the M2M4 estimator. The performance of the AREKF method is demonstrated under various conditions and compared with that of conventional KF-based and phase-locked loop-based methods. Simulation results validate the effectiveness and robustness of the proposed method as well as its superiority over conventional approaches. Real-time experiment results demonstrated the effectiveness of the proposed method in practical applications.