Advantage of extended kalman filter over discrete kalman filter in dynamic state estimation of power system network

Abstract

The Kalman filter is a set of mathematical equations which are used to estimate the state of a system to minimize the mean of the squared error. In this paper Kalman filtering is used for the estimation of states of IEEE 14 bus power system network. We considered Voltage and its angle at all buses as states of the system. This paper presents the advantages of EKF over DKF by comparing both estimation methods. In EKF the exact nonlinear Process and measurement functions can be considered for estimation where it is limited to only linear functions in case of DKF. Because of this limitation using DKF we can estimate only one state of the power system but not both. The approximation of actual for applying DKF leads to the presence of single state i.e the angle of voltage at all buses of the network. In addition to this limitation, accuracy can also be improved from DKF to EKF. As voltage and its angle are not dynamic in nature the measurements are taken as constant at all instants of time during our observation. International Science Press.