PNNL

Abstract

Accurate and robust estimation of dynamic states is essential for monitoring and controlling a power grid. To achieve higher accuracy and robustness, this paper proposes a hybrid estimation approach to estimate the dynamic states of synchronous generators using multiple models, classified into a set of local models and a wide-area model. The proposed estimation approach, first, estimates dynamic states using the extended Kalman filter (EKF) based on a local model and a wide-area model. Then, at each time step, probability indexes, which quantify the likelihood of both local and wide-area models, are determined using hypothesis testing based on measurements innovations. Finally, using the weighted fix approach, the states estimated from the local and wide-area models are combined based on their probability indexes. Simulation studies using the two-area four-machine system shows that the proposed approach can improve estimation accuracy and increase robustness against model errors.