Simultaneous state and input estimation of a synchronous machine using the Extended Kalman Filter with unknown inputs

The states of a synchronous machines, specially the rotor angle and rotor speed variables, are the important reference quantities in the wide area control of power system. Therefore, the accurate information about these variables could help the power system to enhance the system security, stability and reliability. For estimating the angle and speed variables, the traditional EKF method requires that the input signals be measured or available, which may not always be the case. In synchronous machines, for example, the exciter output voltage E_fd, may not be available for measuring in all cases. As a result, in this paper, the Extended Kalman Filter with Unknown Inputs (EKF-UI) technique is developed for identifying and estimating the states and the unknown inputs of the synchronous machine simultaneously using Phasor Measurement Unit (PMU) quantities. Simulation results show the accuracy of the EKF-UI method in both state and unknown input estimation. Finally, some efforts also have been done to demonstrate the EKF-UI method capability in parameter estimation of the system.