Centralized Dynamic State Estimation Using a Federation of Extended Kalman Filters With Intermittent PMU Data From Generator Terminals

Abstract

An improved dynamic state estimation scheme that performs estimation for the full plant (states of a generator, exciter field voltage, and governor mechanical torque) using intermittent data from a phasor measurement unit (PMU) connected at generator terminal is presented. Overall fourth-order generator model is assumed in an extended Kalman filter (EKF), while first-order governors and excitation systems are assumed for simplicity of large-scale implementation. State estimation is performed using the EKF with random PMU data dropouts and known inputs, i.e., secondary reference signals P ref and V ref provided to a power plant by the network control center from economic dispatch. The state estimation scheme has been extended to all generators in network and DSE is performed using a computationally decentralized federation of EKFs at a centralized phasor data concentrator where PMU data are aggregated while dealing with a specified stochastic dropout rate. Required modifications have, thus, been made to standard EKF formulation to account for communication channel interruption and inherent delays. Simulation studies performed on the benchmark IEEE 9 and 39 bus system demonstrated performance and resilience of the proposed centralized EKF-based estimation technique. We also found that a centralized estimator can lead to improved wide-area instability indices derived from state estimates rather than PMU data directly.