PMU analytics for decentralized dynamic state estimation of power systems using the Extended Kalman Filter with Unknown Inputs

The rotor angle and rotor speed estimation of synchronous generators is a key for developing practical local or wide-area control of power system. The critical information in this context is the input signals such as field voltage and mechanical torque which are not available from easily available terminal phasor measurement unit (PMU) signals. To overcome these issues, the Extended Kalman Filter with Unknown Inputs, referred to as the EKF-UI technique, is employed in this paper for decentralized dynamic state estimation of a synchronous machine states using terminal active and reactive powers, voltage phasor and frequency measurements. It is demonstrated that using the decentralized EKF-UI scheme, synchronous machine states can be estimated accurately enough to enable wide-area power system stabilizers (WA-PSSs). Simulation results on Hydro-Quebec simplified network highlight the efficiency of the proposed method under fault conditions with electromagnetic transients and full-order generator models in realistic multi-machine setups.