Dynamic State Estimation With Model Uncertainties Using $H_\infty$ Extended Kalman Filter

When implementing Kalman filters to track system dynamic state variables, the dynamical model is assumed to be accurate. However, this assumption may not hold true as power system dynamical model is subjected to various uncertainties, such as varying generator transient reactance in different operation conditions, uncertain inputs, or noise statistics. As a result, the performance of Kalman-type filters can be degraded significantly. To bound the influence of these uncertainties, this letter proposes an $H_\infty$ extended Kalman filter (HEKF) based on the robust control theory. An approach to tune the parameter of HEKF is presented as well. Numerical results on the IEEE 39-bus system demonstrate the effectiveness of the HEKF.