Adaptive Two-stage Unscented Kalman Filter for Dynamic State Estimation of Synchronous Generator Under Cyber Attacks Against Measurements  

作　　者：Dongchen Hou Yonghui Sun Venkata Dinavahi Yi Wang 

机构地区：[1]the School of Electrical and Power Engineering,Hohai University,Nanjing 210098,China [2]the Department of Electrical and Computer Engineering,University of Alberta,Edmonton,AB T6G 2V4,Canada [3]the School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China [4]the Henan Engineering Research Center of Power Electronics and Energy Systems,Zhengzhou 450001,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第5期1408-1418,共11页现代电力系统与清洁能源学报（英文）

基　　金：supported by the National Natural Science Foundation of China(No.62073121);the National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid(No.U1966202);the Six Talent Peaks High Level Project of Jiangsu Province(No.2017-XNY-004);the Natural Sciences and Engineering Research Council(NSERC)of Canada。

摘　　要：This paper develops an adaptive two-stage unscented Kalman filter(ATSUKF)to accurately track operation states of the synchronous generator(SG)under cyber attacks.To achieve high fidelity,considering the excitation system of SGs,a detailed 9~(th)-order SG model for dynamic state estimation is established.Then,for several common cyber attacks against measurements,a two-stage unscented Kalman filter is proposed to estimate the model state and the bias in parallel.Subsequently,to solve the deterioration problem of state estimation performance caused by the mismatch between noise statistical characteristics and model assumptions,a multi-dimensional adaptive factor matrix is derived to modify the noise covariance matrix.Finally,a large number of simulation experiments are carried out on the IEEE 39-bus system,which shows that the proposed filter can accurately track the SG state under different abnormal test conditions.

关 键 词：Cyber attack dynamic state estimation Kalman filtering synchronous generator(SG) unscented transformation 

分 类 号：TM31[电气工程—电机] TN713[电子电信—电路与系统]