Dynamic State Estimation Using Event-Trigger Master-Slave Nonlinear Filter for WAMS Applications

Abstract

The real-time state estimation becomes greatly important with the wide application of phasor measurement unit (PMU) in distributed generation (DG) for wide-area measurement systems (WAMS). In view of estimation, particle filter (PF) is capable of providing the best performance but at the cost of heavy computation burden. Besides, the growing grid size sustainably boosts the amount of data communication from PMU, causing the congestion. An event-trigger master-slave nonlinear filter (ET-MSNF) is proposed to guarantee the estimation accuracy and get the communication bandwidth relieved. The local slave filter at the generator node carries out the local estimation and event-trigger strategy using unscented transformation, which is identical to the center slave. The master filter at the center is designed using Monte Carlo method to improve the center’s estimation accuracy by the cooperation with the center slave. Such master-slave filtering structure can fully utilize the computation capability both at the center and node. Simulation on the standard IEEE 39-bus system verify the performance of ET-MSNF.