TY - GEN
T1 - Dynamic state estimation using event-trigger master-slave nonlinear filter for WAMS applications
AU - Yuan, Qing
AU - Zhang, Fengdi
AU - Gong, Hengheng
AU - Li, Luyu
AU - Li, Sen
AU - Liao, Xiaozhong
AU - Li, Zhen
AU - Liu, Xiangdong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/30
Y1 - 2018/10/30
N2 - 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.
AB - 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.
KW - Event-trigger
KW - master-slave nonlinear filter
KW - particle filter
KW - unscented transformation
KW - wide-area measurement systems
UR - https://www.scopus.com/pages/publications/85057041803
U2 - 10.1109/DDCLS.2018.8515998
DO - 10.1109/DDCLS.2018.8515998
M3 - Conference contribution
AN - SCOPUS:85057041803
T3 - Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018
SP - 1089
EP - 1094
BT - Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018
Y2 - 25 May 2018 through 27 May 2018
ER -