Accurate power system state estimation (PSSE) is an essential prerequisite for reliable operation of power systems. Different from static PSSE, dynamic PSSE can exploit past measurements based on a dynamical state evolution model, offering improved accuracy and state predictability. A key challenge is the nonlinear measurement model, which is often tackled using linearization, despite divergence and local optimality issues. In this work, a moving-horizon estimation (MHE) strategy is advocated, where model nonlinearity can be accurately captured with strong performance guarantees. To mitigate local optimality, a semidefinite relaxation approach is adopted, which often provides solutions close to the global optimum. Numerical tests show that the proposed method can markedly improve upon an extended Kalman filter (EKF)-based alternative.
|Original language||English (US)|
|Journal||IEEE Power and Energy Society General Meeting|
|State||Published - Oct 29 2014|
|Event||2014 IEEE Power and Energy Society General Meeting - National Harbor, United States|
Duration: Jul 27 2014 → Jul 31 2014
Bibliographical notePublisher Copyright:
© 2014 IEEE.
- Dynamic power system state estimation
- moving-horizon state estimation
- semidefinite relaxation