Journal of Modern Power Systems and Clean Energy
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ISSN 2196-5625 CN 32-1884/TK
ISSN 2196-5625 CN 32-1884/TK
Yanbo Chen
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaHao Chen
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaYang Jiao
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaJin Ma
School of Electrical and Information Engineering, University of Sydney, Sydney 2006, AustraliaYuzhang Lin
Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01852, USA1.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
This work was supported in part by National Natural Science Foundation of China (No. 52077076), and in part by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (No. LAPS2021-18).
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.
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