A Cubature Kalman Filter Based Power System Dynamic State Estimator

This paper proposes an application of the cubature Kalman filter (CKF) to the power system dynamic state estimation (PSDSE) utilizing the measurements from the remote terminal units as well as the phasor measurement units. The CKF process utilizes the spherical cubature and Gaussian quadrature rules to estimate the probability density functions of the state space and the measurement space. This helps in linearization of the nonlinear measurement function without loss of accuracy. The CKF does not require formation of the Jacobian and Hessian matrices to execute the PSDSE, which saves the execution time. A state forecasting technique is utilized to forecast the states during the interval between two time instants of receiving the measurement sets from the field. This helps in estimating the states of the power system during the period when the field measurements are not available. The effectiveness of the application of the CKF to the PSDSE has been demonstrated on IEEE 30 bus system and 246 bus Northern Regional Power Grid Indian system.