ET-SRCKF-Based Dynamic State Estimation for Cyber-Physical Distribution Systems With Delayed Measurements

This paper studies the dynamic state estimation problem for cyber-physical distribution systems (CPDSs) with false data injection attacks (FDIAs) and delayed measurements. In view of the characteristics of multiple measurement types, the equivalent current measurement transformation technique is adopted to make the measurement equation be expressed in the form of linear measurement model. Based on the mixed measurements of phasor measurement units (PMUs) and distribution remote terminal units (DRTUs), a novel model is constructed using Bernoulli distributed random variables to describe the delay phenomena. Further, in order to improve the transmission efficiency of the measurement data, an mechanism is introduced in the network transmission process to minimise the amount of data transmission in the network while ensuring the performance of system state estimation. A measurement model based on the event-triggered mechanism is developed, and an event-triggered square root cubature Kalman filter (ET-SRCKF) algorithm incorporating delayed measurements is designed to implement the state estimation of CPDSs, which can obtain the optimal estimation of the states under delayed measurements. Finally, simulated examples are conducted on the IEEE 33-bus test system, and the effectiveness of the proposed method is illustrated by numerical simulations.