Zhang Yi,Zhao Wei,Li Chuandong,et al.Graph convolutional network based voltage sag assessment of power grid[J].Electric Power Automation Equipment,2026,46(05):191-198.
Zhang Yi,Zhao Wei,Li Chuandong,et al.Graph convolutional network based voltage sag assessment of power grid[J].Electric Power Automation Equipment,2026,46(05):191-198. DOI: 10.16081/j.epae.202512025.
Graph convolutional network based voltage sag assessment of power grid
Traditional assessment methods require extensive fault simulations to be redone whenever there are changes in the operating modes of the power grid,which cannot meet the real-time requirements. To address these issues,a graph convolutional network(GCN) based voltage sag assessment method is proposed. A voltage sag feature graph is constructed from the existing simulation data during voltage sag faults under current operating modes,which serves as the input for the model. A GCN based assessment model is built to excavate the complex nonlinear mapping between the power grid operating mode,fault information,and node voltage sag based on the network topology relationship. Then the developed model is employed to assess voltage sag level of each node after the change of operating condition such as generator unit start-stop and load level variations. Through case studies of the IEEE 39-bus system and the actual grid in a region of East China,the accuracy of the proposed method is demonstrated,and the assessment time can be effectively reduced by 98.8 %,which significantly improves the efficiency of power grid voltage sag assessment.
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