Estimation:

Dynamic state estimation introduces dynamic models for real-time power grid operation. We formulated the dynamic state estimation problem as a Kalman filter process. Both extended Kalman filter (EKF) and ensemble Kalman filter (EnKF) techniques were applied to estimate dynamic states. The dynamics of a power system can be modeled as a set of non-linear differential algebraic equations. Applying EKF and EnKF, the dynamic states can be estimated using a prediction-correction process.

Calibration:

Develop an integrated tool suite consisting of two key modules, model validation/verification and parameter calibration. If any model deficiency is identified, the parameter calibration module is used to identify and correct the inaccurate parameter values. The designed calibration procedures include three steps: (1) sanity check, (2) trajectory-sensitivity analysis, and (3) automatic parameter calibration based on advanced ensemble Kalman filter (EnKF) algorithm.