Extended set-membership filter for power system dynamic state estimation

A new method for power system dynamic state estimation is presented. It is based on the application of the extended set-membership filter (ESMF). The ESMF provides an on-line nonlinear guaranteed estimation that assumes that noise sources are unknown but bounded, rather than stochastic as in probabilistic estimation algorithms such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). Thus, the ESMF provides 100% confidence to the estimated states for the safety and reliability of power systems. In this paper, the ESMF is derived and demonstrated by using two different single machine infinite bus (SMIB) systems. The performance of the ESMF is compared with the classical UKF by using one SMIB system modeled as a second-order nonlinear dynamic equation. The feasibility of the ESMF method with unknown inputs is also presented on the other SMIB system, in which the exciter output voltage may be unavailable but bounded. The ESMF method is also tested on a multi-machine system model to demonstrate its effectiveness.

► The extended set-membership filter (ESMF) is employed for power system dynamic state estimation.
► The ESMF provides 100% confidence to the estimated states for the safety and reliability of power systems.
► The proposed method is tested on two different single machine infinite bus systems and a multi-machine system.
► Simulation results demonstrate the effectiveness of the proposed method.