TY - GEN
T1 - Event-trigger extended Kalman filter design for distributed generation network with limited communication
AU - Xin, Xing
AU - Li, Luyu
AU - Li, Sen
AU - Li, Zhen
AU - Li, Jian
AU - Chen, Zhen
AU - Liu, Xiangdong
N1 - Publisher Copyright:
© 2017 Technical Committee on Control Theory, CAA.
PY - 2017/9/7
Y1 - 2017/9/7
N2 - For distributed generation (DG) network, it is important to estimate the real-time states. The information-centric networking (ICN) is established to take charge of the communication of DG network. However, the assumption of ideal communication between sensors and the estimation center cannot be guaranteed due to the communication constraints of ICN with the increasing DG network. A conventional algorithm, which reduces the communication burden in ICN, is to drop the observation of each smart grid in a random way. However, the accuracy of this algorithm recession decays rapidly with the increasing drop rate. To guarantee an appropriate estimation accuracy when the drop rate increases, this paper introduces the event-trigger strategy into the estimation algorithm. An event-trigger extended Kalman filter (ET-EKF) is established in this paper to adapt the nonlinearity of DG system. ET-EKF reduces the communication burden and achieves an appropriate estimation accuracy at the same time. Finally, its feasibility and performance are demonstrated using the standard IEEE 39-bus system with phasor measurement units (PMUs).
AB - For distributed generation (DG) network, it is important to estimate the real-time states. The information-centric networking (ICN) is established to take charge of the communication of DG network. However, the assumption of ideal communication between sensors and the estimation center cannot be guaranteed due to the communication constraints of ICN with the increasing DG network. A conventional algorithm, which reduces the communication burden in ICN, is to drop the observation of each smart grid in a random way. However, the accuracy of this algorithm recession decays rapidly with the increasing drop rate. To guarantee an appropriate estimation accuracy when the drop rate increases, this paper introduces the event-trigger strategy into the estimation algorithm. An event-trigger extended Kalman filter (ET-EKF) is established in this paper to adapt the nonlinearity of DG system. ET-EKF reduces the communication burden and achieves an appropriate estimation accuracy at the same time. Finally, its feasibility and performance are demonstrated using the standard IEEE 39-bus system with phasor measurement units (PMUs).
KW - Event-trigger extended Kalman filter (ET-EKF)
KW - distributed generation network
KW - phasor measurement unit (PMU)
KW - real-time estimation
KW - smart grids
UR - http://www.scopus.com/inward/record.url?scp=85032207858&partnerID=8YFLogxK
U2 - 10.23919/ChiCC.2017.8027599
DO - 10.23919/ChiCC.2017.8027599
M3 - Conference contribution
AN - SCOPUS:85032207858
T3 - Chinese Control Conference, CCC
SP - 1717
EP - 1722
BT - Proceedings of the 36th Chinese Control Conference, CCC 2017
A2 - Liu, Tao
A2 - Zhao, Qianchuan
PB - IEEE Computer Society
T2 - 36th Chinese Control Conference, CCC 2017
Y2 - 26 July 2017 through 28 July 2017
ER -
