Event-triggered distributed dynamic state estimation with imperfect measurements over a finite horizon

This study addresses distributed state estimation problems for a power system subject to imperfect measurements over a finite horizon. The power system under discussion is assumed to be a time-variant model with varying delays, random non-linearity, and external disturbances. Imperfect measurements through the sensor network include both sensor saturations and signal quantisation. A set of novel distributed state estimators were designed, where each of them utilises the information from both the local sensor node and its neighbours according to the topology. To cater for the various package dropouts that are possible, a random process is introduced during the information transmission between each sensor node and the local estimator. In addition, a novel event-triggered mechanism is also developed for energy reduction. The main work focuses on the analysis and synthesis of event-triggered robust state estimators for the system being addressed, such that the estimation error is bounded in the mean square. In comparison to the existing literature, the transient performances over a finite horizon are emphasised here and a recursive algorithm is provided to obtain the estimator gains. Finally, a power system example is presented to validate the proposed estimator design laws.