Stochastic Event-Triggered Cubature Kalman Filter for Power System Dynamic State Estimation

Abstract

Accurate dynamic state estimation (DSE) plays an important role in power systems. Although various filtering methods, such as unscented Kalman filter (UKF) and particle filter (PF), have been applied for DSE based on phasor measurement units, they occupy a huge communication bandwidth without specific concern. In order to alleviate this communication burden, the event-triggered cubature Kalman filter (CKF) is proposed based on the stochastic event-triggered schedule in this brief. Based on the developed nonlinear event-triggered schedule, the CKF further provides more accurate estimation than UKF and has lower computational complexity than PF. The proposed filter can effectively reduce the communication rate while ensuring the accuracy of filtering. Finally, the standard IEEE 145-bus system is utilized to verify the feasibility and performance of the proposed method.