 Physics-informed graphical neural network for power system state estimationQuang-Ha Ngo, Bang L.H. Nguyen, Tuyen V. Vu, Jianhua Zhang and Tuan Ngo Applied Energy, 2024, vol. 358, issue C, No S0306261923019669 Abstract: State estimation is highly critical for accurately observing the dynamic behavior of the power grids and minimizing risks from cyber threats. However, existing state estimation methods encounter challenges in accurately capturing power system dynamics, primarily because of limitations in encoding the grid topology and sparse measurements. This paper proposes a physics-informed graphical learning state estimation method to address these limitations by leveraging both domain physical knowledge and a graph neural network (GNN). We employ a GNN architecture that can handle the graph-structured data of power systems more effectively than traditional data-driven methods. The physics-based knowledge is constructed from the branch current formulation, making the approach adaptable to both transmission and distribution systems. The validation results of three IEEE test systems show that the proposed method can achieve lower mean square error more than 20% than the conventional methods. Keywords: Physics-informed neural networks; Dynamic state estimation; Graph neural networks; Kalman filter (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:358:y:2024:i:c:s0306261923019669 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2023.122602 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.