Constrained Robust Unscented Kalman Filter for Generalized Dynamic State Estimation

Due to physical or control-related limitations, some dynamic state variables are constrained, such as the regulated voltage, the exciter reference voltage, etc. In addition, there exists a set of algebraic constraints that exactly confines the evolution of the system states. However, a systematic way to fully consider all inequality and equality constraints is lacking in the existing dynamic state estimation (DSE) literature. This paper proposes a general constrained robust DSE framework that is able to deal with various equality and inequality constraints. The project operator is integrated with the pseudo-measurements formulation in a unique manner to address the inequality and equality constraints, respectively, within the unscented Kalman filter (UKF) framework, which relies on the unscented transformation and is derivative-free. To this end, the derivative-free robust UKF is extended to address all the constraints while maintaining its robustness to measurement noise and bad data. Numerical results on the IEEE 39-bus system show that the proposed method exhibits the following benefits: first, improved accuracy of the state estimates in the presence of measurement noise; second, enhanced convergence speed and ability to address weakly observed dynamic state variables; and third, enhanced capability to deal with bad data and cyber attacks.