Distribution System State Estimation Based on Power Flow-Guided GraphSAGE

Baitong Zhai, Dongsheng Yang, Bowen Zhou () and Guangdi Li
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Baitong Zhai: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Dongsheng Yang: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Bowen Zhou: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Guangdi Li: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Energies, 2024, vol. 17, issue 17, 1-15

Abstract: Acquiring real-time status information of the distribution system forms the foundation for optimizing the management of power system operations. However, missing measurements, bad data, and inaccurate system models present a formidable challenge for distribution system state estimation (DSSE) in practical applications. This paper proposes a physics-informed graphical learning state estimation approach, to address these limitations by integrating power flow equations and GraphSAGE. The generalization ability of GraphSAGE for unknown nodes is used to perform inductive learning of measurement information. For unseen measurement points in the training set, the simulation proves that the proposed approach can still satisfactorily predict the state quantity. The training process is guided by power flow equations to ensure it has physical significance. Additionally, the possibility of applying the proposed approach to an actual distribution area is explored. Equivalent preprocessing of the three-phase voltage measurement data of the actual distribution area is conducted to improve the estimation accuracy of the transformer measurement points and simplify the computation required for state estimation.

Keywords: state estimation; distribution system; GraphSAGE; physics-informed graphical learning; power flow guidance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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