TY - GEN
T1 - Distributed Incremental Quasi-Newton Algorithm for Power System State Estimation
AU - Bai, Yu
AU - Li, Wenling
AU - Zhang, Bin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In this paper, we propose a distributed incremental quais-Newton (D-IQN) algorithm for multi-area power system state estimation (MASE). Maximum correntropy criterion (MCC) is used in objective function in order to address non-Gaussian noise. Incremental quais-Newton (IQN) is applied to solve state estimation in each area. In the inter-area communication networks, consensus+innovation strategy is adopted to form a distributed pattern. In this way, each area carries out a local state estimation with limited information exchange with its neighboring areas. As a fully distributed algorithm, no central coordinator is needed here. Based on this peer-to-peer communication paradigm, accurate estimation results are obtained and the privacy of each area remains well-preserved. Numerical experiments are carried out on 118-bus systems. The results show that the algorithm is effective for non-Gaussian noise and outperforms other methods such as distributed Broyden-Fletcher-Goldfarb-Shanno (BFGS), Gauss-Newton and WLS method.
AB - In this paper, we propose a distributed incremental quais-Newton (D-IQN) algorithm for multi-area power system state estimation (MASE). Maximum correntropy criterion (MCC) is used in objective function in order to address non-Gaussian noise. Incremental quais-Newton (IQN) is applied to solve state estimation in each area. In the inter-area communication networks, consensus+innovation strategy is adopted to form a distributed pattern. In this way, each area carries out a local state estimation with limited information exchange with its neighboring areas. As a fully distributed algorithm, no central coordinator is needed here. Based on this peer-to-peer communication paradigm, accurate estimation results are obtained and the privacy of each area remains well-preserved. Numerical experiments are carried out on 118-bus systems. The results show that the algorithm is effective for non-Gaussian noise and outperforms other methods such as distributed Broyden-Fletcher-Goldfarb-Shanno (BFGS), Gauss-Newton and WLS method.
KW - Distributed algorithm
KW - Maximum correntropy criterion
KW - Power system
KW - quasi-Newton
UR - https://www.scopus.com/pages/publications/85127675552
U2 - 10.1109/ICCSS53909.2021.9721947
DO - 10.1109/ICCSS53909.2021.9721947
M3 - 会议稿件
AN - SCOPUS:85127675552
T3 - 2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021
SP - 369
EP - 374
BT - 2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021
Y2 - 10 December 2021 through 12 December 2021
ER -