Dynamic event-based forecasting-aided state estimation for active distribution systems subject to limited communication resource

In this paper, the dynamic event-based forecasting-aided state estimation (FASE) method is developed to deal with the state estimation problem of the active distribution system (ADS) subject to communication constraints and non-linear measurements. The proposed method first constructs a state-space model of the ADS to describe system state time evolution. Secondly, to use communication resources more effectively, the dynamic event-triggered scheme (ETS) is exploited to schedule the data transmission. Aiming at the problem of the ADS in the presence of the non-linear measurement, the Gaussian integral is approximated by the spherical cubature rule to obtain the mean and covariance of the state variables after non-linear transformation. Moreover, the upper bound of the estimation error covariance containing non-triggering errors is derived, and then minimized by suitably designing filter gain, thus developing the dynamic event-triggered cubature Kalman filter (DET-CKF) algorithm to perform state estimation for ADSs. Finally, a series of simulation experiments are conducted to verify the effectiveness of the developed FASE method.