新能源出力的快速变化引起电网电压频繁波动，严重威胁电网的安全与经济运行。为此，提出了一种基于图卷积网络（graph convolutional network，GCN）的实时无功功率优化方法。首先，构建了考虑节点重要性的多目标无功功率优化模型。基于此，建立无功功率优化模型的图表示，并结合优化问题重构邻接矩阵。然后，基于GCN算法映射出最优解集，并利用改进CRITIC-AHP-TOPSIS组合赋权算法选取最优解，获得实时优化策略。最后，以改进的IEEE 39节点系统和实际电网为例，验证了所提方法不仅具有求解速度快和避免陷入局部最优解的优势，而且获得了更理想的电压偏差和有功功率网损，保障新型电力系统运行的安全性和经济性。

The rapid fluctuations in renewable energy output cause frequent voltage oscillations in power grids, severely threatening the safe and economical operation of power grids. To address this issue, a real-time reactive power optimization method based on graph convolutional networks （GCN） is proposed. Firstly, a multi-objective reactive power optimization model considering the importance of nodes is constructed. Based on this, the graph representation of the reactive power optimization model is established, and the adjacency matrix is restructured in conjunction with the optimization problem. Then, the optimal solution set is mapped using the GCN algorithm, and the improved CRITIC-AHP-TOPSIS combined weighting algorithm is employed to select the optimal solution, forming a real-time optimization strategy. Finally, using the modified IEEE 39-node system and an actual power grid as examples, the proposed method is verified. The results show that the method not only has the advantages of fast solving speed and avoiding local optima but also achieves more favorable voltage deviation and active power loss, ensuring the safety and economy of new power system operation.