Event-Triggered Approach to Dynamic State Estimation of a Synchronous Machine Using Cubature Kalman Filter

In this brief, an online discrete-time event-triggered cubature Kalman filter (DECKF) is designed to estimate the state of a synchronous generator. First, we define a fourth-order nonlinear model for a single synchronous generator, then we design an event-triggered mechanism to transfer data through the communication channel when the triggered error is above a certain preestablished threshold. We then design a nonlinear discrete-time CKF for the nonlinear system. We show that the data communication between the sensors and the state estimator is reduced, and the state estimation error is bounded if one selects a proper event-triggered threshold. An example is given to verify the performance of the proposed filter.