  This paper proposes a decentralized dynamic state estimation (DSE) algorithm
with bimodal Gaussian mixture measurement noise. The decentralized DSE is
formulated using the Ensemble Kalman Filter (EnKF) and then compared with the
unscented Kalman filter (UKF). The performance of the proposed framework is
verified using the WSCC 9-bus system simulated in the Real Time Digital
Simulator (RTDS). The phasor measurement unit (PMU) measurements are streamed
in real-time from the RTDS runtime environment to MATLAB for real-time
visualization and estimation. To consider the data corruption scenario in the
streaming process, a bi-modal distribution containing two normal distributions
with different weights and variances are added to the measurements as the noise
component. The performances of both UKF and EnKF are then compared for by
calculating the mean-squared-errors (MSEs) between the actual and estimated
states.
