Robust Forecasting-Aided State Estimation Considering Uncertainty in Distribution System  

作　　者：Dongchen Hou Yonghui Sun Linchuang Zhang Sen Wang 

机构地区：[1]College of Energy and Electrical Engineering,Hohai University,Nanjing 210098,China

出　　处：《CSEE Journal of Power and Energy Systems》2024年第4期1632-1641,共10页中国电机工程学会电力与能源系统学报（英文）

基　　金：partially supported by the National Natural Science Foundation of China under Grant 62073121;partially supported by National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid under Grant U1966202;partially supported by Six Talent Peaks High Level Project of Jiangsu Province under Grant 2017-XNY-004.

摘　　要：With the development of the smart grid,the distribution system operation conditions become more complex and changeable.Furthermore,due to the influence of observation outliers and uncertain noise statistics,it is more difficult to grasp the dynamic operation characteristics of distribution system.In order to address these problems,by using projection statistics and the noise covariance updating technology based on the Sage-Husa noise estimator,for distribution power system with outliers and uncertain noise statistics,a robust adaptive cubature Kalman filter forecasting-aided state estimation method is proposed based on generalized-maximum likelihood type estimator.Furthermore,an adaptive strategy,which can enhance the filtering accuracy under normal conditions,is presented.In the simulation part,the branch parameters and node load parameters of the test system are appropriately modified to simulate the asymmetry of the three-phase branch parameters and the asymmetry of the three-phase loads.Finally,through simulation experiments on the improved test system,it is verified that the robust forecasting-aided state estimation method,presented in this paper,can effectively perceive the actual operating state of the distribution network in different simulation scenarios.

关 键 词：Cubature Kalman filter distribution power system forecasting-aided state estimation projection statistics 

分 类 号：TM73[电气工程—电力系统及自动化] TN713[电子电信—电路与系统]