Data-driven Robust State Estimation Through Off-line Learning and On-line Matching  

作　　者：Yanbo Chen Hao Chen Yang Jiao Jin Ma Yuzhang Lin 

机构地区：[1]State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,School of Electrical&Electronic Engineering,North China Electric Power University,Beijing 102206,China [2]School of Electrical and Information Engineering,University of Sydney,Sydney 2006,Australia [3]Department of Electrical and Computer Engineering,University of Massachusetts,Lowell,MA 01852,USA

出　　处：《Journal of Modern Power Systems and Clean Energy》2021年第4期897-909,共13页现代电力系统与清洁能源学报（英文）

基　　金：This work was supported in part by National Natural Science Foundation of China(No.52077076);in part by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(No.LAPS2021-18).

摘　　要：To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning stage, a linear regression equation is presented by clustering historical data from supervisory control and data acquisition (SCADA), which provides a guarantee for solving the over-learning problem of the existing DDSE methods;then a novel robust state estimation method that can be transformed into quadratic programming (QP) models is proposed to obtain the mapping relationship between the measurements and the state variables (MRBMS). The proposed QP models can well solve the problem of collinearity in historical data. Furthermore, the off-line learning stage is greatly accelerated from three aspects including reducing historical categories, constructing tree retrieval structure for known topologies, and using sensitivity analysis when solving QP models. At the on-line matching stage, by quickly matching the current snapshot with the historical ones, the corresponding MRBMS can be obtained, and then the estimation values of the state variables can be obtained. Simulations demonstrate that the proposed DDSE method has obvious advantages in terms of suppressing over-learning problems, dealing with collinearity problems, robustness, and computation efficiency.

关 键 词：Robust state estimation historical snapshot off-line learning on-line matching COLLINEARITY 

分 类 号：TM76[电气工程—电力系统及自动化] TP274[自动化与计算机技术—检测技术与自动化装置]