Power Systems Dynamic State Estimation using Central Difference Filter

Abstract

A nonlinear Sigma point Kalman filter known as Central Difference Filter which considers only first order Taylor series approximation with the help of interpolation formula, have been employed here for the first time during dynamic state estimation of power systems states. This paper also exhibits a comparative performance analysis of two estimation techniques namely Central difference filter (CDF) and Cubature Kalman filter technique (CKF) during power systems dynamic state estimation. The estimation is performed employing measurements from Remote terminal units (RTU) and Phasor measurement units (PMU). The whole simulation process is carried out for IEEE 30 bus test system. Holts two parameter linear exponential technique which often utilized as a state forecasting technique has been used here to forecast the systems state at the prediction step. The superiority of CDF over CKF, has been illustrated here on context of computation time and estimation accuracy.