TY - GEN
T1 - Robust State Estimation for Power Systems Using Quasi-Newton Method
AU - Bai, Yu
AU - Li, Wenling
AU - Li, Xiaoming
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/14
Y1 - 2021/5/14
N2 - This article addresses the state estimation problem in power system under non-Gaussian noise environment. This estimator is designed based on maximum correntropy criterion (MCC), which exhibits the robustness with reference to non-Gaussian noise. To avoid computing the inverse matrix of Hessian matrix, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is adopted to propose a centralized power system state estimation algorithm. For the reason of security, privacy and further reducing the computation burden, adapt-then-combine (ATC) strategy is applied to design a distributed state estimation algorithm based on the centralized algorithm. The effectiveness of the proposed method is verified by the numerical results of IEEE 14-bus and IEEE 118-bus systems under uniformly distributed noise, t-distribution noise and impulsive noise.
AB - This article addresses the state estimation problem in power system under non-Gaussian noise environment. This estimator is designed based on maximum correntropy criterion (MCC), which exhibits the robustness with reference to non-Gaussian noise. To avoid computing the inverse matrix of Hessian matrix, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is adopted to propose a centralized power system state estimation algorithm. For the reason of security, privacy and further reducing the computation burden, adapt-then-combine (ATC) strategy is applied to design a distributed state estimation algorithm based on the centralized algorithm. The effectiveness of the proposed method is verified by the numerical results of IEEE 14-bus and IEEE 118-bus systems under uniformly distributed noise, t-distribution noise and impulsive noise.
KW - Distributed algorithm
KW - Maximum correntropy criterion
KW - Power system state estimation
KW - quasi-Newton methods
UR - https://www.scopus.com/pages/publications/85114203948
U2 - 10.1109/DDCLS52934.2021.9455656
DO - 10.1109/DDCLS52934.2021.9455656
M3 - 会议稿件
AN - SCOPUS:85114203948
T3 - Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021
SP - 1328
EP - 1333
BT - Proceedings of 2021 IEEE 10th Data Driven Control and Learning Systems Conference, DDCLS 2021
A2 - Sun, Mingxuan
A2 - Zhang, Huaguang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021
Y2 - 14 May 2021 through 16 May 2021
ER -